ArticlePDF Available

Prudent public health intervention strategies to control the coronavirus disease 2019 transmission in India: A mathematical model-based approach

Authors:
ResearchGate Logo

This article is featured on the COVID-19 research community page

View COVID-19 community

Abstract and Figures

Background & objectives: :Coronavirus disease 2019 (COVID-19) has raised urgent questions about containment and mitigation, particularly in countries where the virus has not yet established human-to-human transmission. The objectives of this study were to find out if it was possible to prevent, or delay, the local outbreaks of COVID-19 through restrictions on travel from abroad and if the virus has already established in-country transmission, to what extent would its impact be mitigated through quarantine of symptomatic patients?" Methods: :These questions were addressed in the context of India, using simple mathematical models of infectious disease transmission. While there remained important uncertainties in the natural history of COVID-19, using hypothetical epidemic curves, some key findings were illustrated that appeared insensitive to model assumptions, as well as highlighting critical data gaps. Results: :It was assumed that symptomatic quarantine would identify and quarantine 50 per cent of symptomatic individuals within three days of developing symptoms. In an optimistic scenario of the basic reproduction number (R00) being 1.5, and asymptomatic infections lacking any infectiousness, such measures would reduce the cumulative incidence by 62 per cent. In the pessimistic scenario of R0=4, and asymptomatic infections being half as infectious as symptomatic, this projected impact falls to two per cent. Interpretation & conclusions: :Port-of-entry-based entry screening of travellers with suggestive clinical features and from COVID-19-affected countries, would achieve modest delays in the introduction of the virus into the community. Acting alone, however, such measures would be insufficient to delay the outbreak by weeks or longer. Once the virus establishes transmission within the community, quarantine of symptomatics may have a meaningful impact on disease burden. Model projections are subject to substantial uncertainty and can be further refined as more is understood about the natural history of infection of this novel virus. As a public health measure, health system and community preparedness would be critical to control any impending spread of COVID-19 in the country.
Content may be subject to copyright.
1
© 2020 Indian Journal of Medical Research, published by Wolters Kluwer - Medknow for Director-General, Indian Council of Medical Research
Prudent public health intervention strategies to control the coronavirus
disease 2019 transmission in India: A mathematical model-based
approach
Sandip Mandal1, Tarun Bhatnagar3, Nimalan Arinaminpathy4, Anup Agarwal1, Amartya Chowdhury1,
Manoj Murhekar, Raman R. Gangakhedkar2 & Swarup Sarkar1
1Translational Global Health Policy Research Cell (Department of Health Research), 2Division of Epidemiology
& Communicable Diseases, Indian Council of Medical Research, New Delhi, 3ICMR School of Public Health,
ICMR-National Institute of Epidemiology, Chennai, Tamil Nadu, India & 4Department of Infectious Disease
Epidemiology, School of Public Health, Imperial College, St Mary’s Hospital, London, UK
Received February 27, 2020
Background & objectives: Coronavirus disease 2019 (COVID-19) has raised urgent questions about
containment and mitigation, particularly in countries where the virus has not yet established human-to-
human transmission. The objectives of this study were to nd out if it was possible to prevent, or delay,
the local outbreaks of COVID-19 through restrictions on travel from abroad and if the virus has already
established in-country transmission, to what extent would its impact be mitigated through quarantine of
symptomatic patients?
Methods: These questions were addressed in the context of India, using simple mathematical models
of infectious disease transmission. While there remained important uncertainties in the natural history
of COVID-19, using hypothetical epidemic curves, some key ndings were illustrated that appeared
insensitive to model assumptions, as well as highlighting critical data gaps.
Results: It was assumed that symptomatic quarantine would identify and quarantine 50 per cent of
symptomatic individuals within three days of developing symptoms. In an optimistic scenario of the
basic reproduction number (R0) being 1.5, and asymptomatic infections lacking any infectiousness, such
measures would reduce the cumulative incidence by 62 per cent. In the pessimistic scenario of R0=4, and
asymptomatic infections being half as infectious as symptomatic, this projected impact falls to two per cent.
Interpretation & conclusions: Port-of-entry-based entry screening of travellers with suggestive clinical
features and from COVID-19-aected countries, would achieve modest delays in the introduction of
the virus into the community. Acting alone, however, such measures would be insucient to delay the
outbreak by weeks or longer. Once the virus establishes transmission within the community, quarantine
of symptomatics may have a meaningful impact on disease burden. Model projections are subject to
substantial uncertainty and can be further rened as more is understood about the natural history of
infection of this novel virus. As a public health measure, health system and community preparedness
would be critical to control any impending spread of COVID-19 in the country.
Key words Airport screening - COVID-19 - deterministic model - mathematical model - mitigation - quarantine - transmission
Indian J Med Res, Epub ahead of print
DOI: 10.4103/ijmr.IJMR_504_20
Quick Response Code:
[Downloaded free from http://www.ijmr.org.in on Monday, March 23, 2020, IP: 59.94.234.25]
2 INDIAN J MED RES, 2020
As per the World Health Organization (WHO),
85,403 cases of coronavirus disease 2019 (COVID-19)
were reported globally, as of February 29, 2020,
including 79,394 cases (2838 deaths) from China and
6009 cases (86 deaths) from 53 other countries/territories/
areas1. Initially, all of the cases detected in countries
other than China were linked to infected cases from
China, with subsequent generation of cases in some
of the countries, the latest being Japan, South Korea
and Italy. Considering the high population mobility
through air travel and the documented person-to-person
transmission, the WHO provided an advisory on exit
screening in countries with the ongoing transmission
of COVID-19 and entry screening in countries
without transmission, including screening for the
signs and symptoms of respiratory infection with
focus on temperature screening to detect potential
suspects who would require further laboratory tests
for the conrmation of infection2. As per a stochastic,
worldwide, air transportation network dynamic model,
India ranks 17th among the countries at the highest risk
of importation of COVID-19 through air travel3. The
probability of an infected air traveller to come to India
as the nal destination was 0.209 per cent, with the
highest relative import risk in Delhi (0.064%) followed
by Mumbai, Kolkata, Bengaluru, Chennai, Hyderabad
and Kochi3. This in the context of an epidemic that has
already set in and travel from infected areas continues.
The Ministry of Health and Family Welfare (MoHFW)
of India had initially advised to refrain from travelling
to China and quarantine of those coming from China4.
Those returning from Wuhan, China, after January 15,
2020 were to be tested for COVID-19. Those feeling sick
within a month of return from China were advised to report
to the nearest health facility in addition to maintaining
self-isolation at home5. Initially, thermal entry screening
of passengers from China was established at 21 airports
across the country with universal screening for all ights
from China, Hong Kong, Singapore, Thailand, Japan,
South Korea, Iran and Italy. Symptomatic passengers
were advised to volunteer for screening. Similar screening
was in place at international seaports6. Till February 29,
2020, three cases were reported from India7.
In the absence of a licensed vaccine or effective
therapeutics for COVID-19, in addition to the
non-pharmaceutical measures of hand hygiene and
cough etiquettes, quarantine becomes a critical
strategic containment and mitigation intervention
towards the early detection and isolation of cases to
break the chain of transmission and slow down the
spread of the outbreak. This analysis was done with
the following objectives: (i) is it feasible to prevent, or
delay, the local outbreaks in India through restrictions
on travel from countries with COVID-19 transmission;
and (ii) in the event that COVID-19 transmission
becomes established in India, the extent to which its
impact could be mitigated through quarantine.
Material & Methods
This analysis was based on a simple
Susceptible-Exposed-Infectious-Recovered (SEIR)
model to capture the natural history of COVID-19
and its transmission dynamics. The model structure
is summarised in Fig. 1, with the following governing
equations:
dS S
dt
= −
dE S rE
dt
= −
dI rE I I
dt

=−−
I kE
NN

= +
where the compartments are as follows: susceptible
(S); exposed and infectious but not yet symptomatic
(E); infected and symptomatic (I) and recovered
(R). Model parameters are as follows: among those
Fig. 1. Summary of the model structure used to represent coronavirus
disease 2019 transmission and control in Indian cities. The population
in each metropolitan area is divided into different compartments,
representing states of disease, with ows between compartments
given by the rates shown in the diagram. Thus, susceptible individuals
(S), upon acquiring infection, enter a state of asymptomatic infection
(E) and with some delay develop symptomatic disease (I). It is
assumed that a proportion p of symptomatic cases is subject to
quarantine [I(q)] and the remainder [I(n)] is not. The relative size of
these two populations (p) reects the coverage of quarantine efforts.
Individuals in I(q) are quarantined with an average quarantine delay
(1/δ). Finally, individuals may be cured (R) or die as per recovery
rate (γ) or mortality rate (µ), respectively. Those people who are
successfully quarantined (Q) do not contribute to onward infection.
[Downloaded free from http://www.ijmr.org.in on Monday, March 23, 2020, IP: 59.94.234.25]
MANDAL et al: MATHEMATICAL MODELS FOR COVID-19 INTERVENTIONS IN INDIA 3
exposed, per-capita rate of developing symptoms (r);
among symptomatics, per-capita rates of recovery and
death (γ and µ, respectively) and the average number
of infections caused per day per symptomatic case (β)
and the infectiousness of exposed/asymptomatic cases,
relative to symptomatic (k).
With the evolving understanding of the natural
history of COVID-19 infection, it was assumed that
all infections would go through an asymptomatic
stage lasting three days on an average, followed by
a symptomatic stage, also lasting three days on an
average. Previous work has shown that the extent of
transmission that occurs before symptoms develop can
be an important factor in the feasibility of control8.
The estimates for the basic reproduction number (R0)
range between 1.5 and 4.98-16. In the current study, we
sought to capture a wide range of possible scenarios by
adopting two contrasting scenarios, as listed in Table I.
Containment: Port-of-entry screening model: First,
a deterministic epidemic was simulated in Wuhan,
China, governed by the equations above, to inform
projections for the daily introductions of COVID-19
that would arrive on Indian airports. This simulation
provided estimates for the prevalence of infection
in China, denoted by E(source) (t) and I(source) (t), for the
proportion of the population having asymptomatic and
symptomatic infection, respectively, at time t.
Then the following stochastic process was
simulated for transmission in India: (i) A transmission
process governed by the equations above, using a
simple Gillespie algorithm17 to translate these to
stochastic dynamics, assuming that infection events are
independent of one another; and (ii) Initial conditions
being zero prevalence and universal susceptibility, but
with a time series of ME (τ), MI (τ), introductions of
cases of E and I on day τ into the community, for all τ >
0 (these being arrivals from China who have not been
stopped at the airport).
To calculate the latter, it was assumed that each day,
there were a total of A arrivals from the source region
into Indian airports, ignoring seasonality or secular
temporal trends. Recalling that E(source) (t) and I(source) (t)
are proportions, then on any given day, the proportion
of airport arrivals that is infected and asymptomatic
is E(source) (t). If we assume that symptomatic cases
are m times less likely to travel than those without
symptoms, then the proportion of arrivals being infected
and symptomatic is I(source) (t)/m. Further, assuming
that as a result of airport screening, a proportion pE
of infected and asymptomatic cases is stopped at the
airport before entering the community, and likewise for
a proportion pI of infected and symptomatic cases.
Putting these factors together, the number of cases
of E being introduced into the community in India, per
day would be calculated as:
Introductions of E on day τ ~ Bin (A,q[τ])
where ‘Bin’ denotes a binomial distribution, and
( )
( )
( )
d
q E t dt
=
We modelled similarly for the number of
introductions of I on day τ, but with the adjustment m
described above.
For traveller demographics, we assume
conservatively that A=500, meaning that on an average,
500 passengers are arriving per day in Indian airports,
from areas in China where COVID-19 transmission is
established; the prevalence of asymptomatic infection
in international arrivals is the same as in their city of
origin and the prevalence of symptomatic infection
is half as much (m=½), assuming that symptomatics
are half as likely to travel. Airline transportation
data suggested that, on an average, there were
3565 passengers arriving from the entire China per
day, in Indian airports, during the period from October
2018 to March 201918. We expect this number to have
been reduced substantially following recent travel
restrictions, but the relevant data are not yet publicly
available. Thus, we expect our assumption to be an
underestimate.
Under the given scenarios for the proportion of
asymptomatic and symptomatic cases that would go
undetected by screening, we simulated the stochastic
epidemic that would occur in India as a result of the
daily introductions and estimated the average ‘time to
epidemic’ as the number of days to reach a prevalence
of 1000 cases. This threshold, although arbitrary,
Table I. Model parameters for optimistic and pessimistic
scenarios of coronavirus disease-19 transmission in India
Parameters Optimistic
scenario
Pessimistic
scenario
Basic reproduction number (R0)1.5 4
Infectiousness of asymptomatic
cases, relative to symptomatic
case (k)
0 0.5
[Downloaded free from http://www.ijmr.org.in on Monday, March 23, 2020, IP: 59.94.234.25]
4 INDIAN J MED RES, 2020
represents a level at which it is clear that transmission
has been established in India.
Mitigation: Within-country model: In the event that
COVID-19 started spreading in India, we developed a
mathematical model to simulate the transmission dynamics
in the four most populated metropolitan areas (Delhi,
Mumbai, Kolkata and Bengaluru metropolitan areas) in
India, as well as their population connectivity. We chose
to focus on these population centres on the assumption
that the introduction of COVID-19 was most likely to
occur in international transportation hubs, and thus that
these cities were most likely to be the focal points of
initial COVID-19 transmission in the country.
As an intervention, we modelled a ‘quarantine
of symptomatics’ scenario wherein a proportion p of
symptomatic cases was quarantined within an average
of d days of developing symptoms. To incorporate this
intervention, we adapted the model equations above,
as follows:
i
dS S
=
i
ii i
dE S rE
dt
= −
( )
( ) ( ) ( )
q
q qq
i
ii i i
dI rpE I I I
dt
 =−−
( )
( )
( ) ( )
1
n
nn
i
ii i
dI r pE I I
dt
=− −−
( )
q
i
i ii
dQ
I QQ
dt
 
= −−
( ) ( )
qn
i
ii i
dR
IIQ
dt
=++
i
I kE
NN

= +
( ) ( )
( )
/
qn
i ij j j j j
ij
c I I kE N= ++


where the subscript i represents city i; I(q) is the number
with symptomatic infection who will self-quarantine after
an average delay of d days; I(n) is the number who are
symptomatic yet do not quarantine and the rate parameter
δ is the inverse of the average quarantine delay, d. The
infectiousness of exposed/asymptomatic cases, relative to
symptomatic cases, is termed as relative infectiousness (k).
Finally, cij is the connectivity between cities i
and j. We used domestic airline transportation data18
as a proxy for cij, while also conducting a sensitivity
analysis to address intercity travel through other
means, including rail and road. These coefcients (cij)
were estimated as a proxy for the frequency of daily
population movement between cities as a proportion
of the population of those cities. In sensitivity analysis,
we assumed ten times the rates shown in Table II, to
address the potential contributions from the lack of rail
and road travel data.
Using this deterministic model, as summarized in
Fig. 1, we simulated the introduction of COVID-19
and the resulting epidemic in one of the metropolitan
areas. We simulated the epidemic in various scenarios
for the proportion of symptomatics being quarantined;
the delay to quarantine and the natural history scenarios
are shown in Table I.
We present the hypothetical scenario for
COVID-19 transmission and interventional effects
in Delhi metropolitan area, as an illustration. We
estimated the time to hypothetical peak epidemic
in days. As an intervention, we modelled a scenario
where a given proportion of symptomatic cases
(50% at most) could self-quarantine, within a given
delay after developing symptoms (at least two days).
The indicators for the impact of intervention on the
hypothetical epidemic scenario were reduction in
cumulative incidence, peak prevalence mitigation
(proportional reduction in the highest number
of prevalent cases) and attack rate mitigation
(proportional reduction in cumulative incidence).
Results
Containment: Airport screening: Fig. 2 shows the
delays that could be achieved in the introduction of
infection within India, as a result of screening airport
arrivals. If symptomatic arrivals alone were screened
(blue curve), the model projections for the time to
epidemic ranged from 45 to 47.7 days. For illustration,
we also examined the impact of screening among
asymptomatic individuals (red curve). Results showed
Table II. Model coefcient for connectivity between cities
Cij Delhi Mumbai Kolkata Bengaluru
Delhi 1 0.00045 0.00029 0.00058
Mumbai 0.00048 1 0.00019 0.00052
Kolkata 0.00032 0.00018 1 0.00025
Bengaluru 0.00058 0.00052 0.00025 1
Cij, connectivity between cities i and j
[Downloaded free from http://www.ijmr.org.in on Monday, March 23, 2020, IP: 59.94.234.25]
MANDAL et al: MATHEMATICAL MODELS FOR COVID-19 INTERVENTIONS IN INDIA 5
that identifying at least 75 per cent of the asymptomatic
individuals was needed, in order to delay the
within-country outbreak by an appreciable amount.
Additional detection of 90 per cent asymptomatic
individuals would delay the average time to epidemic
by 20 days (Table III). These levels of coverage
among asymptomatic cases are practically infeasible,
requiring almost all passengers from the identied
ights to be screened. However, this hypothetical
scenario offers a helpful approach for explaining
the lack of impact from addressing symptomatic
cases alone (Fig. 2, blue curve). Any containment
strategy focused on symptomatic infections, no matter
how comprehensively tends to be negated by the
asymptomatic infections that escape detection and can
go on to cause onward transmission in the community.
Mitigation: Within-country interventions: Fig. 3
illustrates the hypothetical epidemic dynamics that
would result in the four metropolitan areas, from an
outbreak beginning in Delhi metropolitan area, and
under an ‘optimistic’ scenario for transmission. The
Figure illustrates the seeding of transmission in other
cities that could arise, as a result of air transportation
between these populations. The Figure also illustrates
the impact of a hypothetical intervention, wherein 50
per cent of symptomatic cases are quarantined (whether
voluntarily or through screening and testing), within
an average of three days of developing symptoms.
Such measures could reduce the peak prevalence
substantially, thus minimizing the pressure on public
health services. As a consequence, the intervention
has the effect of ‘attening’ the epidemic curve,
distributing cases over a longer duration than in the
absence of intervention. The intervention could reduce
the cumulative incidence by 62 per cent. We next
illustrate how these impacts may vary, under different
transmission and intervention scenarios.
Impact of quarantine of symptomatics: In the
‘optimistic’ scenario, quarantining 50 per cent of
symptomatic cases within three days of developing
symptoms would reduce the cumulative incidence by
62 per cent and the peak prevalence by 89 per cent.
By contrast in a ‘pessimistic’ scenario, the projected
impact on the cumulative incidence falls to two per
cent and the peak prevalence by eight per cent. The
corresponding impact on peak prevalence is similarly
low, as shown in Fig. 4.
Fig. 5 shows that the duration of the outbreak would
be much lower in the scenario of ‘no intervention’
compared to ‘intervention’. As illustrated in Fig. 3, the
overall effect of symptomatic quarantine is to atten the
outbreak and increase the duration of the outbreak.
Table III. Alternate scenarios for the effect of airport entry screening of symptomatic and asymptomatic passengers on the delay in
average time to epidemic (days to reach a prevalence of 1000 cases) in India by R0 and relative infectiousness of asymptomatics
Parameters Delay in average time to epidemic (days)
R0Relative infectiousness,
asymptomatic versus
symptomatic
All symptomatic COVID-19
identied, but zero
diagnosis in asymptomatics
All symptomatic COVID-19
identied, with 50 per cent
diagnosis in asymptomatics
All symptomatic COVID-19
identied, with 90 per cent
diagnosis in asymptomatics
2 0.5 1.2 5.7 16
2 0.1 2.9 7.4 20
4 0.5 0.5 1.9 5.7
4 0.1 0.8 2.9 7.9
COVID-19, coronavirus disease 19
Fig. 2. Model projections for the time to epidemic in India
(the time to reach a prevalence of 1000 cases), under different
scenarios for the intensity of port-of-entry screening. The left half
of the gure illustrates the effect, on epidemic timing, of screening
symptomatic passengers alone; the right half illustrates the additional
effect of diagnosing coronavirus disease-19 amongst asymptomatic
passengers, assuming full screening of symptomatic passengers
(infeasible, but illustrative). Solid lines show central estimates,
whereas dashed lines span 95 per cent of simulated uncertainty
intervals.
[Downloaded free from http://www.ijmr.org.in on Monday, March 23, 2020, IP: 59.94.234.25]
6 INDIAN J MED RES, 2020
Fig. 3. Model projections for the hypothetical epidemic dynamics (symptomatic prevalence over time) with and without intervention under
different scenarios for epidemiologic parameters considering an intervention, in which 50 per cent of the symptomatic cases are isolated
within three days of developing symptoms.
Fig. 4. Model projections for the per cent reduction in hypothetical peak prevalence and per cent reduction in hypothetical cumulative incidence
by initiation of quarantine of symptomatics within two, three and four days under the ‘optimistic’ (A) and ‘pessimistic’ (B) scenarios described
in the main text.
B
A
[Downloaded free from http://www.ijmr.org.in on Monday, March 23, 2020, IP: 59.94.234.25]
MANDAL et al: MATHEMATICAL MODELS FOR COVID-19 INTERVENTIONS IN INDIA 7
Discussion
The focus of our analysis was not towards
predicting the burden of COVID-19 cases but to
identify rational intervention strategies that might
work towards control of the outbreak in India. We
modelled the potential impact of containment strategy
of point-of-entry screening and a mitigation response
through symptomatic screening on hypothetical
COVID-19 transmission scenario in India. Our results
suggest that in order to have an appreciable effect
on delaying the establishment of transmission of
COVID-19 in India, airport arrival screening will need
to have near-complete capture of incoming COVID-19
cases, including asymptomatic cases. Although not
practically feasible using the currently available tools,
our results provide a hypothetical illustration of the
additional benet of identifying asymptomatic cases:
if they escape any containment effort, they would
tend to negate the effects of that effort, by the onward
transmission that they can cause. Presently, there is
no accurate, rapid test for COVID-19 that could be
deployed in this setting, to reach the required levels
of detection among asymptomatic cases; the only way
to reach 90 per cent diagnosis among asymptomatic
arrivals may be through isolation and quarantine of all
arrivals from specied origin airports. Resources may
be better spent on the mitigation of infection in the
community.
Recent studies indicate that airport screening may
not be able to sufciently detect COVID-19-infected
travellers. Quilty et al19 estimated that 46 per cent
(95% condence interval: 36 to 58) of infected
travellers would not be detected by thermal screening at
airport exit and entry, depending on incubation period,
sensitivity of exit and entry screening and proportion
of asymptomatic cases. Gostic et al20 estimated that
travel screening would miss more than half of the
infected travellers on account of being asymptomatic
and being unaware of exposure, emphasizing the
need for post-travel symptom tracking among them.
Our study adds to this by considering the population
implications of such leakages in arrival screening.
Our analysis shows that, even if symptomatic cases
are comprehensively identied and quarantined, the
delay in epidemic timing within India would be in
days and not weeks. According to the data shared by
the Delhi Health Department21, till February 13, 2020,
17 of 5700 (0.3%) passengers, who had arrived from
China and other COVID-19-affected countries prior
to the beginning of airport screening from January 15,
2020, were found symptomatic and hospitalized, while
the rest were advised for home isolation. The status
of another 885 passengers remains unknown21. Entry
screening or travel restrictions may be benecial in
reducing the risk of outbreak in countries with relatively
low connectivity to China, and our study illustrates the
critical importance of community-based measures to
detect potential cases and prevent transmission.
We also examined the potential impact of quarantine
of symptomatics, in controlling transmission within
India, with a focus on four major metropolitan areas.
Our results suggest that it may be possible to interrupt
the transmission of COVID-19 in India, but only in the
most optimistic scenarios (for R0 and for coverage).
Even with high R0 and suboptimal coverage,
symptomatic quarantine can still achieve meaningful
reductions in peak prevalence, resulting in ‘spreading
out’ of the outbreak. This would make it easier to cope
Fig. 5. Projected duration of epidemic (days) for the scenarios with and without symptomatic quarantine at 50 per cent coverage in three days
by R0 and relative infectiousness of asymptomatic cases. Here, the ‘epidemic duration’ is measured as the duration (in days) over which the
prevalence of symptomatic infection is >1 case.
[Downloaded free from http://www.ijmr.org.in on Monday, March 23, 2020, IP: 59.94.234.25]
8 INDIAN J MED RES, 2020
with the peak demand on health services. However,
such measures would have very little effect on the
overall epidemic size. The actual numerical impact will
be highly sensitive to the natural history of COVID-19,
the parameters for which are very uncertain at present.
The WHO Scientic and Technical Advisory Group
for Infectious Hazards has recommended continuation
of the containment strategy and monitoring for
the community transmission of COVID-1922. It
recommends close monitoring of the effectiveness and
social acceptance of public health strategies to control
COVID-19 transmission in the light of its evolving
epidemiological understanding, including engagement
of vulnerable populations, and intensied active
surveillance22.
Continuous follow up of passengers returning
from COVID-19-affected countries and their contact
tracing for the emergence of suggestive symptoms
would put a high strain on the healthcare system, more
so in the eventuality of the introduction of community
transmission. The increasing numbers would make it
impractical to use laboratory testing to conrm each
case, and therefore, use of symptomatic surveillance
should become the primary public health strategy to
detect and respond in the most effective and timely
manner. We could draw examples from the syndromic
surveillance approach for inuenza-like illness in the
context of H1N123. In practice, this could be achieved
either through public advisories for sick individuals
to self-quarantine, along with active engagement with
the community, or through intensive surveillance
for symptoms, followed by testing and quarantine.
A combination of both approaches is likely to be
needed, although promoting self-quarantine is likely
to be more sustainable in the event that transmission
becomes widespread. Engagement of local volunteers
and community-based organizations can provide the
much-needed boost to the efforts of the public health
system. Considering the widespread use of mobile
phones in the country, mobile applications can be
used to self-monitoring and sharing of symptom
information on a real-time basis. The same was done
for monitoring the passengers on the cruise ship off the
Japanese coast24.
With the evolving understanding of COVID-19
epidemiology, especially the proportion of
asymptomatic infected cases, it is difcult to predict
the number of beds required or ventilators necessary
for COVID-19 cases at this stage. As per reports from
other affected countries, we may expect eight to ten
severe and 40-50 non-severe COVID-19 cases for every
death25,26. In a closed setting of similar nature as that
on the cruise ship ‘Diamond Princess,’ we may expect
26 per cent of the entire population to get infected and
one in 450 infected individuals to die27. We deduce
that around ve per cent of the infected patients will
require intensive care and half of those admitted in the
intensive care unit will require mechanical ventilation.
Over time, once the model is validated, appropriate
numbers can be generated for healthcare planning.
It is pertinent that frontline healthcare workers
are identied and trained before the outbreak sets
in. Health and life insurance should be announced
for healthcare workers if they contract COVID-19.
Considering the reports of a high number of infected
healthcare workers, measures should be taken to
build biosecurity wards and prepare for the outbreak
in earnest. Resources should be earmarked; adequate
supplies should be procured before the outbreak gains
momentum. Healthcare workers should be trained in
the use of personal protective equipment, screening
of asymptomatic contacts, isolation measures and
management of COVID-19 cases. Public health
measures should be initiated at multiple levels,
including but not limited to public messaging, and
community health worker-based education.
Limitations of the model: As with any modelling
study, our analysis has some limitations to note. The
mean duration of asymptomatic and symptomatic
stages is very much uncertain. Some infections may
be subclinical and never develop symptoms. In the
port-of-entry screening model, we adopted simple
assumptions on the number of daily arrivals from
non-coronavirus-affected areas due to lack of data.
However, considering that we have only used data
for airport arrivals and in particular from China, these
assumptions are likely to be underestimates in the current
situation where people are travelling from many other
countries that are now reporting COVID-19 cases, and
are thus conservative with respect to our conclusions;
higher numbers of daily arrivals would tend to narrow
the gap in epidemic timing, between baseline and
interventional scenarios. Other important uncertainties
include natural history parameters, for example, the
average duration of infection; the incubation period
and the case fatality rate. Though we have tried to
address some of these uncertainties through examining
different scenarios for transmission, yet we caution that
our model ndings may also be sensitive to these other
[Downloaded free from http://www.ijmr.org.in on Monday, March 23, 2020, IP: 59.94.234.25]
MANDAL et al: MATHEMATICAL MODELS FOR COVID-19 INTERVENTIONS IN INDIA 9
parameters. As more data become available about this
new virus, subsequent modelling work can be rened
accordingly.
For the country-level model, for simplicity, we
created hypothetical scenarios only in four metropolitan
areas that have the highest population density. These
areas cover only about seven per cent of the total
population of India. We ignored the rural population
surrounded by these areas and their connectivity.
Future work to address this gap will benet from more
systematic information on the rates of population ow
between these different settings, data that were not
available for our current study. We have simplied
our meta-population model by considering constant
connectivity between different cities, ignoring
age-dependent mobility among the population. How
seasonality will change the endemicity of COVID-19
is still unknown and hence not considered in the
model. Although there appear to be differences in the
immune responses of children compared to adults, for
simplicity, this model has not accounted the disease
prevalence with age structure.
Comparison of our projected gures with data
from countries such as Japan, the Republic of Korea
and Iran can help to validate our model, assuming
similar transmission dynamics in India. It may be
noted that our analysis is based on the available global
epidemiological parameters from the initial phase of
the outbreak. However, we believe that the predicted
direction of the model-based impact of the proposed
interventions would remain unaffected, although the
onset, magnitude and timing of the simulated epidemic
may change, even with the use of updated parameter
values from the evolving global situation of COVID-19
epidemic. Validation of mathematical models using
real-time data is important to gauge the accuracy of
predicted transmission dynamics of infectious diseases.
While some models for Ebola virus disease27 provided
fairly reasonable estimates, recent COVID-19 models28
were inconsistent in their prediction.
Public health implications: At present, it is not clear to
what extent the COVID-19 epidemic would establish
itself in India. As the introduction of cases may take
anywhere from a minimum of 20 days to a few months to
be visible, we need to enhance surveillance and prepare
the community in a proportionate way that is neither
alarmist nor complacent. The critical concerns are the
efciency and timeliness of quarantine and isolation
and the challenges of detection of COVID-19 with
symptoms similar to many other lower respiratory tract
infections. There is a need to engage community-based
organizations that can take up the work of symptomatic
surveillance, as well as raising awareness of the need
for self-quarantine where possible, and referral to
hospital where necessary, till infection is conrmed.
Till that time, assurance of food and supplies should be
given following examples of such practices in Kerala29.
It is pertinent to engage with the media on a proactive
basis with the provision of facts promptly such that
reporting of these events does not create a picture of the
overwhelming burden of COVID-19 in the country and
lead to undue anxiety among the population that may
negatively inuence self-quarantine. Health authorities
need to be on alert and be prepared to closely monitor
the situation with the establishment of an intensied
surveillance. We advocate for a rational, exible and
resilient approach that is sensitive to the outbreak
stage as the health system prepares for the control of
COVID-19 transmission in India.
Financial support & sponsorship: None.
Conicts of Interest: None.
References
1. World Health Organization. COVID-19 (COVID-19) Situation
Report - 40. Geneva: WHO; 2020. Available from: https://
www.who.int/docs/default-source/coronaviruse/situation
-reports/20200229-sitrep-40-covid-19.pdf?sfvrsn=849d0665_2,
accessed on February 29, 2020.
2. World Health Organization. Updated WHO advice for
international trac in relation to the outbreak of the
COVID-19. Geneva: WHO; 2020. Available from: https://
www.who.int/ith/COVID-19_advice_for_international_trac
/en/, accessed on February 11, 2020.
3. Event Horizon - COVID-19. Coronavirus COVID=19 Global
Risk Assessment. Available from: http://rocs.hu-berlin.de/
corona/#relative-import-risk, accessed on February 26, 2020.
4. National Centre for Disease Control. Travel Advisory.
5 February, 2020. Available from: https://ncdc.gov.in/
WriteReadData/l892s/63950984511580999086.pdf., accessed
on February 16, 2020.
5. National Centre for Disease Control. COVID-19 outbreak
in China – Travel advisory to travelers visiting China.
Available from: https://ncdc.gov.in/WriteReadData/
l892s/34827556791580715701.pdf, accessed on February 11,
2020.
6. Bhargava B. Sudan P. Prepared for the coronavirus. The
Hindu; 11 February, 2020. Available from: https://www.
thehindu.com/opinion/op-ed/prepared-for-the-coronavirus/
article30785312.ece, accessed on February 15, 2020.
7. Press Information Bureau. Update on COVID-19: Cases
and management. Ministry of Health and Family Welfare,
[Downloaded free from http://www.ijmr.org.in on Monday, March 23, 2020, IP: 59.94.234.25]
10 INDIAN J MED RES, 2020
Government of India; 2020. Available from: https://pib.gov.in/
newsite/pmreleases.aspx?mincode=31, accessed on February
29, 2020.
8. Wu JT, Leung K, Leung GM. Nowcasting and forecasting the
potential domestic and international spread of the 2019-nCoV
outbreak originating in Wuhan, China: A modelling study.
Lancet 2020; 395 : 689-97.
9. Imai N, Cori A, Dorigatti I, Baguelin M, Donnelly CA,
Riley S, et al. Report 3: Transmissibility of COVID-19.
Imperial College London 2020. Available from: https://www.
imperial.ac.uk/media/imperial-college/medicine/sph/ide/
gida-fellowships/Imperial-2019-nCoV-transmissibility.pdf,
accessed on February 17, 2020.
10. Riou J, Althaus CL. Pattern of early human-to-human
transmission of Wuhan 2019-nCoV. bioRxiv 2020.
[doi: 10.1101/2020.01.23.917351].
11. Liu T, Hu J, Kang M, Lin L, Zhong H, Xiao J, et al.
Transmission dynamics of novel coronavirus (2019-nCoV).
bioRxiv 2020. [doi: 10.1101/2020.01.25.919787].
12. Read JM, Bridgen JR, Cummings DA, Ho A, Jewell CP. Novel
coronavirus 2019-nCoV: Early estimation of epidemiological
parameters and epidemic predictions. medRxiv 2020.
[doi: 10.1101/2020.01.23.20018549].
13. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al.
Early transmission dynamics in Wuhan, China, of novel
coronavirus-infected pneumonia. N Engl J Med 2020.
[doi: 10.1056/NEJMoa2001316].
14. Shen M, Peng Z, Xiao Y, Zhang L. Modelling the epidemic
trend of the 2019 novel coronavirus outbreak in China.
bioRxiv 2020. [doi: 10.1101/2020.01.23.916726].
15. Backer JA, Klinkenberg D, Wallinga J. The incubation period
of 2019-nCoV infections among travellers from Wuhan,
China. medRxiv 2020. [doi: 10.1101/2020.01.27.20018986].
16. Linton NM, Kobayashi T, Yang Y, Hayashi K,
Akhmetzhanov AR, Jung S, et al. Incubation period
and other epidemiological characteristics of 2019 novel
coronavirus infections with right truncation: A statistical
analysis of publicly available case data. medRxiv 2020.
[doi: 10.1101/2020.01.26.20018754].
17. Gualtieri AF, Hecht JP. Dynamics and control of infectious
diseases in stochastic metapopulation models. J Life Sci 2011;
5 : 505-8.
18. Directorate General of Civil Aviation. Statistics and reports
– Operator year 2019; Directorate General of Civil Aviation.
Available from: https://dgca.gov.in/digigov-portal/?page=jsp/
dgca/InventoryList/dataReports/aviationDataStatistics/air
Transport/domestic/monthly/index.html, accessed on February
15, 2020.
19. Quilty BJ, Clifford S, Flasche S, Eggo RM; CMMID nCoV
Working Group. Effectiveness of airport screening at detecting
travellers infected with novel coronavirus (2019-nCoV). Euro
Surveill 2020 ; 25.
20. Gostic K, Gomez ACR, Mummah RO,
Kucharski AJ, Lloyd-Smith JO. Estimated effectiveness
of traveller screening to prevent international spread of
2019 novel coronavirus (2019-nCoV). medRxiv 2020.
[doi: 10.1101/2020.01.28.20019224].
21. Press Trust of India. 17 people in Delhi who returned
from abroad before coronavirus screening showed
symptoms, hospitalised. DTNEXT; February 15, 2020.
Available from: https://www.dtnext.in/News/TopNe
ws/2020/02/15172416/1215388/17-people-in-Delhi-who-retu
rned-from-abroad-before-.vpf accessed on February 17, 2020.
22. Heymann DL, Shindo N, WHO Scientic and Technical
Advisory Group for Infectious Hazards. COVID-19: What is
next for public health? Lancet 2020; 395 : 542-5.
23. Elliot A. Syndromic surveillance: The next phase of public
health monitoring during the H1N1 inuenza pandemic? Euro
Surveill 2009; 14 . pii: 19391.
24. Sonnemaker T. The Japanese government gave 2,000
iPhones to passengers stuck on a cruise ship where nearly
200 coronavirus cases have been conrmed. Business
Insider; 15 February, 2020. Available from: https://www.
businessinsider.in/business/news/the-japanese-government-
gave-2000-iphones-to-passengers-stuck-on-a-cruise-ship-
where-nearly-200-coronavirus-cases-have-been-conrmed/
articleshow/74143614.cms, accessed on February 16, 2020.
25. Guan W, Ni Z, Yu H, Liang W, Ou C, He J, et al. Clinical
characteristics of coronavirus disease 2019 in China. N Engl J
Med 2020. [doi: 10.1056/NEJMoa2002032].
26. Coronavirus COVID-19 global cases by the Center for Systems
Science and Engineering (CSSE) at Johns Hopkins University
(JHU). Available from: https://www.arcgis.com/apps/
opsdashboard/index.html#/bda7594740fd40299423467b48e
9ecf6, accessed on February 28, 2020.
27. Ferrández MR, Ivorra B, Ramos AM. Validation of the forecasts
for the spread of the Ebola virus disease 2018-20 (EVD 2018-20)
done with the Be-CoDiS mathematical model; 2020. Available
from: https://www.researchgate.net/prole/Benjamin_Ivor ra/
publication/339443804_Validation_of_the_forecasts_for_
the_spread_of_the_Ebola_virus_disease_2018-20_EVD
_2018-20_done_with_the_Be- CoDiS_mathematical_model/
links/5e52fe18458515072db797a3/Validation-of-the-fore
casts-for-the-spread-of-the-Ebola-virus-disease-2018-20-EV
D-2018-20-done-with-the-Be-CoDiS-mathematical-model.
pdf, accessed on February 27, 2020.
28. Ivorra B, Ramos AM. Validation of the forecasts for the
international spread of the coronavirus disease 2019
(COVID-19) done with the Be-CoDiS mathematical model;
2020. Available from: https://www.researchgate.net/prole/
Benjamin_Ivorra/publication/339314163_Validation_of_the_
forecasts_for_the_international_spread_of_the_coronavirus_
disease_2019_COVID-19_done_with_the_Be-CoDiS_
mathematical_model/links/5e503dd5458515072dafb711/
Validation-of-the-forecasts-for-the-international-spread-of-
the-coronavirus-disease-2019-COVID-19-done-with-the-Be-
CoDiS-mathematical-model.pdf, accessed on February 27,
2020.
29. Maya C. Fighting a virus, yet again: How controlling the
Nipah outbreak helped Kerala to take on COVID-19. The
Hindu; 15 February, 2020. Available from: https://www.
thehindu.com/sci-tech/health/fighting-a-virus-yet-again-
how-controlling-the-nipah-outbreak-helped-kerala-to-take-
on-covid-19/article30825430.ece, accessed on February 18,
2020.
For correspondence: Dr Tarun Bhatnagar, ICMR-National Institute of Epidemiology, Ayapakkam, Chennai 600 077, Tamil Nadu, India
e-mail: tarunbhatnagar@nie.gov.in
[Downloaded free from http://www.ijmr.org.in on Monday, March 23, 2020, IP: 59.94.234.25]
... Different parameterized ARIMA and its variants have been used across studies targeting regions such as India [20,21], Pakistan [22], Saudi Arabia [23], Mainland China, Italy, South Korea, Thailand [24], US, Brazil, Russia, Spain [21], North America, South America, Africa, Asia and Europe [25], Italy, Spain, and France [26], and the most hit countries [27,28]. Furthermore, models such as Susceptible, Ex-posed, Infection and Recover (SEIR), Infection and Recover (SIR) and their variations, and others such as Agent-based models, Curve-fitting and Logistic growth models have also been applied extensively for mathematical modeling of the COVID-19 situations for forecasting purposes [29,30,31,32,33,34]. ...
Preprint
Full-text available
As of writing this paper, COVID-19 (Coronavirus disease 2019) has spread to more than 220 countries and territories. Following the outbreak, the pandemic's seriousness has made people more active on social media, especially on the microblogging platforms such as Twitter and Weibo. The pandemic-specific discourse has remained on-trend on these platforms for months now. Previous studies have confirmed the contributions of such socially generated conversations towards situational awareness of crisis events. The early forecasts of cases are essential to authorities to estimate the requirements of resources needed to cope with the outgrowths of the virus. Therefore, this study attempts to incorporate the public discourse in the design of forecasting models particularly targeted for the steep-hill region of an ongoing wave. We propose a sentiment-involved topic-based methodology for designing multiple time series from publicly available COVID-19 related Twitter conversations. As a use case, we implement the proposed methodology on Australian COVID-19 daily cases and Twitter conversations generated within the country. Experimental results: (i) show the presence of latent social media variables that Granger-cause the daily COVID-19 confirmed cases, and (ii) confirm that those variables offer additional prediction capability to forecasting models. Further, the results show that the inclusion of social media variables for modeling introduces 48.83--51.38% improvements on RMSE over the baseline models. We also release the large-scale COVID-19 specific geotagged global tweets dataset, MegaGeoCOV, to the public anticipating that the geotagged data of this scale would aid in understanding the conversational dynamics of the pandemic through other spatial and temporal contexts.
... [9][10][11][12] Other models were developed to predict the effect of nonpharmaceutical measures on epidemic dynamics. [13][14][15] In India, a number of mathematical models on COVID-19 were proposed and published in peer-reviewed journals and as grey literature during this period of time. [16][17][18][19] This systematic review aims to provide a comprehensive review of existing mathematical models on COVID-19 in India that are published from January 2020 to January 2022. ...
... [9][10][11][12] Other models were developed to predict the effect of nonpharmaceutical measures on epidemic dynamics. [13][14][15] In India, a number of mathematical models on COVID-19 were proposed and published in peer-reviewed journals and as grey literature during this period of time. [16][17][18][19] This systematic review aims to provide a comprehensive review of existing mathematical models on COVID-19 in India that are published from January 2020 to January 2022. ...
Article
Full-text available
Background: More than 278 million cases and more than 5.4 million deaths due to coronavirus disease (COVID-19) were reported worldwide by the end of 2021. More than 34 million cases and more than 478,000 deaths have been reported in India. Epidemiologists, physicians and virologists are working on a number of conceptual, theoretical or mathematical modelling techniques in the battle against COVID-19. Protocol: This systematic review aims to provide a comprehensive review of published mathematical models on COVID-19 in India and the concepts behind the development of mathematical models on COVID-19, including assumptions, modelling techniques, and data inputs. Initially, related keywords and their synonyms will be searched in the Global Literature on Coronavirus Disease database managed by World Health Organisation (WHO). The database includes searches of bibliographic databases (MEDLINE, Scopus, Web of Science, EMBASE etc.,), preprints (MEDRXIV), manual searching, and the addition of other expert-referred scientific articles. This database is updated daily (Monday through Friday). Two independent reviewers will be involved in screening the titles and abstracts at the first stage and full-texts at the second stage, and they will select studies as per the inclusion and exclusion criteria. The studies will be selected for their quality, transparency, and ethical aspects, using the Overview, Design concepts, Details (ODD) protocol and International Society for Pharmacoeconomics and Outcomes Research-Society for Medical Decision Making (ISPOR-SMDM) guidelines. Data will be extracted using standardized data extraction tools and will be synthesized for analysis. Disagreements will be resolved through discussion, or with a third reviewer. Conclusions: This systematic review will be performed to critically examine relevant literature of existing mathematical models of COVID-19 in India. The findings will help to understand the concepts behind the development of mathematical models on COVID-19 conducted in India in terms of their assumptions, modelling techniques, and data inputs.
... Towards that end, the quantitative knowledge of the interactions between states in terms of population flow or the interactions within the state in terms of population density can be thought to be good indicators of the propagation of this disease across states. However, the knowledge regarding the population flow which should take into account not only the airline transportation data, but also various other modes of communication such as railways or roadways can not be expected to be very precise and reliable (Mandal et al., 2020). As an alternative to availing this data, the prediction analysis of the disease can be recast as a network inference problem for a particular network-based epidemic model where useful parameters of the model and the contact pattern can be estimated. ...
Article
Full-text available
Objective of this present study is to predict the COVID-19 trajectories in terms of infected population of Indian states. In this work, a state interaction network of sixteen Indian states with highest number of infected caseload is considered, based on networked Susceptible-Exposed-Infected-Recovered (SEIR) epidemic model. An intervention term has been introduced in order to capture the effect of lockdown with different stringencies at different periods of time. The model has been fitted using least absolute shrinkage and selection operator (LASSO). Machine learning methods have been used to train the parameters of the model, cross-validate the data, and predict the parameters. The predictions of infected population for each of the sixteen states have been shown using data considered from January 1, 2021 till writing this manuscript on June 25, 2021. Finally, the effectiveness of the model is manifested by the calculated mean error and confidence interval.
... It caused millions of death and devastated the economy and society. Despite various measures executed by different countries (Bayham and Fenichel, 2020;Hellewell et al., 2020;Koo et al., 2020;Mandal et al., 2020;Verelst et al., 2020;Zhang et al., 2020), the incidence of COVID-19 continues to surge worldwide. SARS (Severe acute respiratory syndrome) and MERS (Middle east respiratory syndrome) were quickly under control, whereas COVID-19 is still spreading outside China and several other countries. ...
Article
Full-text available
The coronavirus disease 2019 (COVID-19) pandemic has led to unprecedented global challenges. A zero-COVID strategy is needed to end the crisis, but there is a lack of biological evidence. In the present study, we collected available data on SARS, MERS, and COVID-19 to perform a comprehensive comparative analysis and visualization. The study results revealed that the fatality rate of COVID-19 is low, whereas its death toll is high compared to SARS and MERS. Moreover, COVID-19 had a higher asymptomatic rate. In particular, COVID-19 exhibited unique asymptomatic transmissibility. Further, we developed a foolproof operating software in Python language to simulate COVID-19 spread in Wuhan, showing that the cumulative cases of existing asymptomatic spread would be over 100 times higher than that of only symptomatic spread. This confirmed the essential role of asymptomatic transmissibility in the uncontrolled global spread of COVID-19, which enables the necessity of implementing the zero-COVID policy. In conclusion, we revealed the triggering role of the asymptomatic transmissibility of COVID-19 in this unprecedented global crisis, which offers support to the zero-COVID strategy against the recurring COVID-19 spread.
... On 29th June 2020, the coronavirus cases crossed the 1 billion mark worldwide [9]. After registering the first case on 30th January 2020, Coronavirus has become a public health concern in India [10]. By 3rd July 2020, India has registered 625,544 cases [11]. ...
Article
Full-text available
Hygiene has been noticed as one of the most effective procedures against COVID-19 cross-transmission, especially hand hygiene and covering the face with the mask. Therefore, this study tried to peek into the people movements and seeks to understand how people are handling their daily use items like fruits and vegetables, how people are managing unavoidable grooming services, how people are disinfecting themselves after coming from outside, and what all hygienic practices they are following during this pandemic. Furthermore, this study attempts to explore ways through which people are disinfecting their houses. At last, the study seeks to explore the knowledge/information people have about Coronavirus. The study collected primary data through a self-administered questionnaire. A quota sampling technique was used to collect the data. Bivariate analysis was carried out to reach the study findings. Based on the findings, it is the need of the hour to disseminate the information on the use of unhealthy disinfectants as they lack the knowledge about the safe use of various types of cleaners and disinfectants. It is also reiterated that there is an urgency to promote further information on risk factors of Coronavirus among people and compulsion to promote healthy hand hygiene and sanitation practices. There is a need to promote information through mass media and other modes of awareness, such as artwork and announcements.
Article
Full-text available
Mathematical modelling has been a helpful resource for planning public health responses to COVID-19. However, there is a need to improve the accessibility of models built within country contexts in the Global South. Immediately following the overwhelming 'second wave' of COVID-19 in India, we developed a user-friendly, web-based modelling simulator in partnership with the public health experts and health administrators for subnational planning. The purpose was to help policy-makers and programme officials at the state and district levels, to construct model-based scenarios for a possible third wave. Here, we describe our experiences of developing and deploying the simulator and propose the following recommendations for future such initiatives: early preparation will be the key for pandemic management planning, including establishment of networks with potential simulator users. Ideally, this preparedness should be conducted during 'peace time', and coordinated by agencies such as WHO. Second, flexible modelling frameworks will be needed, to respond rapidly to future emergencies as the precise nature of any pandemic is impossible to predict. Modelling resources will, therefore, need to be rapidly adaptable to respond as soon as a novel pathogen emerges. Third, limitations of modelling must be communicated clearly and consistently to end users. Finally, systematic mechanisms are required for monitoring the use of models in decision making, which will help in providing modelling support to those local authorities who may benefit most from it. Overall, these lessons from India can be relevant for other countries in the South-Asian-Region, to incorporate modelling resources into their pandemic preparedness planning.
Article
Background: The emergence of the COVID-19 pandemic has placed a significant burden on everyone. Although dental professionals are at an increased risk of COVID-19 infection, currently, very little is known about how oral health professionals and their professions could be affected by the pandemic. This study aims to investigate dentists' perceptions on present and future dental practice in light of the COVID-19 pandemic. Methods: We conducted an embedded mixed-methods study at Manipal College of Dental Sciences, Mangalore, with Indian dentists registered with the Dental Council of India. Results: Of the 976 participating dentists, 61% were females, 32% were 40 years of age or younger. Nearly half of the respondents (54%) acknowledged that the lockdown measures caused them a severe financial burden, and 56% were seriously concerned about being a source of infection to their family, friends, and community. Although 79% felt very comfortable or somewhat comfortable going back to work, they were all worried that Personal Protective Equipment (PPE) use would increase their financial burden and impact the number of patients seeking care. Even though a vast majority received the necessary information regarding returning to practice from their concerned dental regulatory bodies, some were unsure about the reuse of the PPEs because of the conflicting information they received. Conclusion: The COVID-19 pandemic affected participants' professional lives negatively. Their major concerns were being a source of infection to their families and community. Providing information to dental professionals in a timely manner may prepare dentists to provide safe care to their patients while protecting themselves, their staff, and their families.
Article
SARS-CoV-2 Mpro is one of the most vital enzymes of the new coronavirus-2 (SARS-CoV-2) and is a crucial target for drug discovery. Unfortunately, there is not any potential drugs available to combat the action of SARS-CoV-2 Mpro. Based on the reports HIV-protease inhibitors can be applied against the SARS by targeting the SARS-CoV-1 Mpro, we have chosen few clinically trialed experimental and allophenylnorstatine (APNS) containing HIV-protease inhibitors (JE-2147, JE-533, KNI-227, KNI-272 & KNI-1931), to examine their binding affinities with SARS-CoV-2 Mpro and to assess their potential to check for a possible drug candidate against the protease. Here, we have chosen a methodology to understand the binding mechanism of these five inhibitors to SARS-CoV-2 Mpro by merging molecular docking, molecular dynamics (MD) simulation and MM-PBSA based free energy calculations. Our estimations disclose that JE-2147 is highly effective (ΔGBind = -28.31 kcal/mol) due to an increased favorable van der Waals (ΔEvdw) interactions and decreased solvation (ΔGsolv) energies between the inhibitor and viral protease. JE-2147 shows a higher level of interactions as compared to JE-533 (-6.85 kcal/mol), KNI-227 (-18.36 kcal/mol), KNI-272 (-15.69 kcal/mol) and KNI-1931 (-21.59 kcal/mol) against SARS-CoV-2 Mpro. Binding contributions of important residues (His41, Met49, Cys145, His164, Met165, Glu166, Pro168, Gln189, etc.) from the active site or near the active site regions with ≥1.0 kcal/mol suggest a potent binding of the inhibitors. It is anticipated that the current study of binding interactions of these APNS containing inhibitors can pitch some valuable insights to design the significantly effective anti-SARS-CoV-2 Mpro drugs.Communicated by Ramaswamy H. Sarma.
Article
Full-text available
Background: Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected patients. Methods: We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. Results: The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission. Conclusions: During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.).
Technical Report
Full-text available
Date of the report: 23 February 2020. The World Health Organization (WHO) declared an outbreak of Ebola Virus Disease (EVD) in the Democratic Republic of Congo (DRC) on the 1st of August, 2018. Regarding the emergency of this situation, our research group has performed an analysis of this situation by using the Be-CoDiS model. On the 23rd of July, 2019, we proposed a forecast of the possible evolution of this epidemic. We now validate the forecast with real observations.
Technical Report
Full-text available
28 February 2020: A new and fast spreading coronavirus was detected in Wuhan, Hubei province, in China on December 2019 (see [1]). Regarding the emergency of this situation, our research group (see [2]) has performed an analysis of this situation by using the Be-CoDiS model (see [3, 4]). On 8 February 2020, we proposed two forecasts of the possible evolution of this epidemic. We aim to validate those results with official reported data. From a general point of view: i) From 9 February to 20 February, the observed data are in the range of the values estimated by Forecasts FC1 and FC2. ii) From 21 February up to 27 February, the reported of cases in China are close to those predicted by FC2 and the number of deaths are in the range of the forecasted values. However, focusing on the number of cases worldwide, the gap between the predicted and the observed data is increasing and is not negligible. Regarding the recent changes in the dynamic of the spread worldwide, we need to recalibrate the model in order to update our forecasts. However, we first require gathering more data in order to estimate well the dynamics of the epidemic in recently infected countries to perform reliable new forecasts.
Article
Full-text available
Background: The initial cases of novel coronavirus (2019-nCoV)-infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. Methods: We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. Results: Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9). Conclusions: On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.).
Preprint
Full-text available
Currently, a novel coronavirus 2019-nCoV causes an outbreak of viral pneumonia in Wuhan, China. Little is known about its epidemiological characteristics. Using the travel history and symptom onset of 34 confirmed cases that were detected outside Wuhan, we estimate the mean incubation period to be 5.8 (4.6 - 7.9, 95% CI) days, ranging from 1.3 to 11.3 days (2.5th to 97.5th percentile). These values help to inform case definitions for 2019-nCoV and appropriate durations for quarantine.
Preprint
Full-text available
The geographic spread of 2019 novel coronavirus (COVID-19) infections from the epicenter of Wuhan, China, has provided an opportunity to study the natural history of the recently emerged virus. Using publicly available event-date data from the ongoing epidemic, the present study investigated the incubation period and other time intervals that govern the epidemiological dynamics of COVID-19 infections. Our results show that the incubation period falls within the range of 2–14 days with 95% confidence and has a mean of around 5 days when approximated using the best-fit lognormal distribution. The mean time from illness onset to hospital admission (for treatment and/or isolation) was estimated at 3–4 days without truncation and at 5–9 days when right truncated. Based on the 95th percentile estimate of the incubation period, we recommend that the length of quarantine should be at least 14 days. The median time delay of 13 days from illness onset to death (17 days with right truncation) should be considered when estimating the COVID-19 case fatality risk.
Preprint
Full-text available
Background: Since December 29, 2019, pneumonia infection with 2019-nCoV has rapidly spread out from Wuhan, Hubei Province, China to most others provinces and other counties. However, the transmission dynamics of 2019-nCoV remain unclear. Methods: Data of confirmed 2019-nCoV cases before January 23, 2020 were collected from medical records, epidemiological investigations or official websites. Data of severe acute respiratory syndrome (SARS) cases in Guangdong Province during 2002-2003 were obtained from Guangdong Provincial Center for Disease Control and Prevention (GDCDC). Exponential Growth (EG) and maximum likelihood estimation (ML) were applied to estimate the reproductive number (R) of 2019-nCoV and SARS. Findings: As of January 23, 2020, a total of 830 confirmed 2019-nCoV cases were identified across China, and 9 cases were reported overseas. The average incubation duration of 2019-nCoV infection was 4.8days. The average period from onset of symptoms to isolation of 2019-nCoV and SARS cases were 2.9 and 4.2 days, respectively. The R values of 2019-nCoV were 2.90 (95%CI: 2.32-3.63) and 2.92 (95%CI: 2.28-3.67) estimated using EG and ML respectively, while the corresponding R values of SARS-CoV were 1.77 (95%CI: 1.37-2.27) and 1.85 (95%CI: 1.32-2.49). We observe a decreasing trend of the period from onset to isolation and R values of both 2019-nCoV and SARS-CoV. Interpretation: The 2019-nCoV may have a higher pandemic risk than SARS broken out in 2003. The implemented public-health efforts have significantly decreased the pandemic risk of 2019-nCoV. However, more rigorous control and prevention strategies and measures to contain its further spread.
Preprint
Full-text available
We present a timely evaluation of the Chinese 2019-nCov epidemic in its initial phase, where 2019-nCov demonstrates comparable transmissibility but lower fatality rates than SARS and MERS. A quick diagnosis that leads to case isolation and integrated interventions will have a major impact on its future trend. Nevertheless, as China is facing its Spring Festival travel rush and the epidemic has spread beyond its borders, further investigation on its potential spatiotemporal transmission pattern and novel intervention strategies are warranted.
Preprint
Full-text available
On December 31, 2019, the World Health Organization was notified about a cluster of pneumonia of unknown aetiology in the city of Wuhan, China. Chinese authorities later identified a new coronavirus (2019-nCoV) as the causative agent of the outbreak. As of January 23, 2020, 655 cases have been confirmed in China and several other countries. Understanding the transmission characteristics and the potential for sustained human-to-human transmission of 2019-nCoV is critically important for coordinating current screening and containment strategies, and determining whether the outbreak constitutes a public health emergency of international concern (PHEIC). We performed stochastic simulations of early outbreak trajectories that are consistent with the epidemiological findings to date. We found the basic reproduction number, R_0, to be around 2.2 (90% high density interval 1.4--3.8), indicating the potential for sustained human-to-human transmission. Transmission characteristics appear to be of a similar magnitude to severe acute respiratory syndrome-related coronavirus (SARS-CoV) and the 1918 pandemic influenza. These findings underline the importance of heightened screening, surveillance and control efforts, particularly at airports and other travel hubs, in order to prevent further international spread of 2019-nCoV.
Article
Background: Since Dec 31, 2019, the Chinese city of Wuhan has reported an outbreak of atypical pneumonia caused by the 2019 novel coronavirus (2019-nCoV). Cases have been exported to other Chinese cities, as well as internationally, threatening to trigger a global outbreak. Here, we provide an estimate of the size of the epidemic in Wuhan on the basis of the number of cases exported from Wuhan to cities outside mainland China and forecast the extent of the domestic and global public health risks of epidemics, accounting for social and non-pharmaceutical prevention interventions. Methods: We used data from Dec 31, 2019, to Jan 28, 2020, on the number of cases exported from Wuhan internationally (known days of symptom onset from Dec 25, 2019, to Jan 19, 2020) to infer the number of infections in Wuhan from Dec 1, 2019, to Jan 25, 2020. Cases exported domestically were then estimated. We forecasted the national and global spread of 2019-nCoV, accounting for the effect of the metropolitan-wide quarantine of Wuhan and surrounding cities, which began Jan 23-24, 2020. We used data on monthly flight bookings from the Official Aviation Guide and data on human mobility across more than 300 prefecture-level cities in mainland China from the Tencent database. Data on confirmed cases were obtained from the reports published by the Chinese Center for Disease Control and Prevention. Serial interval estimates were based on previous studies of severe acute respiratory syndrome coronavirus (SARS-CoV). A susceptible-exposed-infectious-recovered metapopulation model was used to simulate the epidemics across all major cities in China. The basic reproductive number was estimated using Markov Chain Monte Carlo methods and presented using the resulting posterior mean and 95% credibile interval (CrI). Findings: In our baseline scenario, we estimated that the basic reproductive number for 2019-nCoV was 2·68 (95% CrI 2·47-2·86) and that 75 815 individuals (95% CrI 37 304-130 330) have been infected in Wuhan as of Jan 25, 2020. The epidemic doubling time was 6·4 days (95% CrI 5·8-7·1). We estimated that in the baseline scenario, Chongqing, Beijing, Shanghai, Guangzhou, and Shenzhen had imported 461 (95% CrI 227-805), 113 (57-193), 98 (49-168), 111 (56-191), and 80 (40-139) infections from Wuhan, respectively. If the transmissibility of 2019-nCoV were similar everywhere domestically and over time, we inferred that epidemics are already growing exponentially in multiple major cities of China with a lag time behind the Wuhan outbreak of about 1-2 weeks. Interpretation: Given that 2019-nCoV is no longer contained within Wuhan, other major Chinese cities are probably sustaining localised outbreaks. Large cities overseas with close transport links to China could also become outbreak epicentres, unless substantial public health interventions at both the population and personal levels are implemented immediately. Independent self-sustaining outbreaks in major cities globally could become inevitable because of substantial exportation of presymptomatic cases and in the absence of large-scale public health interventions. Preparedness plans and mitigation interventions should be readied for quick deployment globally. Funding: Health and Medical Research Fund (Hong Kong, China).