Available via license: CC BY-NC-ND 4.0
Content may be subject to copyright.
1
Community Transmission of SARS-CoV-2 by Fomites: Risks and Risk
1
Reduction Strategies
2
3
4
5
Ana K. Pitol1 , Timothy R. Julian2,3,4
6
7
1 Department of Civil and Environmental Engineering, Imperial College London, United Kingdom
8
2Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
9
3Swiss Tropical and Public Health Institute, Basel, Switzerland
10
4University of Basel, Basel, Switzerland
11
12
13
14
15
16
17
18
19
20
21
22
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.
2
Abstract
23
SARS-CoV-2, the virus responsible for the COVID-19 pandemic, is perceived to be primarily
24
transmitted via person-to-person contact, through droplets produced while talking, coughing, and
25
sneezing. Transmission may also occur through other routes, including contaminated surfaces;
26
nevertheless, the role that surfaces have on the spread of the disease remains contested. Here we use
27
the Quantitative Microbial Risk Assessment framework to examine the risks of community
28
transmission of SARS-CoV-2 through contaminated surfaces and to evaluate the effectiveness of hand
29
and surface disinfection as potential interventions. The risks posed by contacting surfaces in
30
communities are low (average of the median risks 1.6x10-4 - 5.6x10-9) for community infection
31
prevalence rates ranging from 0.2-5%. Hand disinfection substantially reduces relative risks of
32
transmission independently of the disease's prevalence and the frequency of contact, even with low
33
(25% of people) or moderate (50% of people) compliance. In contrast, the effectiveness of surface
34
disinfection is highly dependent on the prevalence and the frequency of contacts. The work supports
35
the current perception that contaminated surfaces are not a primary mode of transmission of SARS-
36
CoV-2 and affirms the benefits of making hand disinfectants widely available.
37
38
Introduction
39
SARS-CoV-2, the virus responsible for the COVID-19 pandemic, is transmitted primarily via person-
40
to-person pathways such as prolonged exposures to respiratory droplets produced while talking,
41
coughing, and sneezing 1,2. Based on the assumption of respiratory-droplet transmission, infection
42
control recommendations include maintaining social/physical distances, wearing masks, case
43
isolation, contact tracing, and quarantine 3. Due to the possibility of transmission through other routes,
44
including airborne and surface-mediated transmission, the WHO recommends taking airborne
45
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
3
precautions for particular settings where aerosols are generated and emphasizes the importance of
46
hand hygiene2. Nevertheless, the role that airborne and surface-mediated transmission have on the
47
spread of the disease remains contested 1,2,4–7.
48
49
Indirect transmission via fomites (contaminated surfaces) contributes to the spread of common
50
respiratory pathogens 8–10 and evidence-to-date suggests fomite transmission is possible for SARS-
51
CoV-2. People infected with SARS-CoV-2 shed the virus into the environment, as evidenced by
52
extensive SARS-CoV-2 RNA detected on surfaces in cruise ships, hospitals, and public spaces in
53
urban areas such as bus stations and public squares 11–15. Infective coronavirus persists in the
54
environment, with experimental evidence of persistence on surfaces ranging from 3 hours to 28 days,
55
depending on environmental factors such as surface material and temperature 16–18. Viruses readily
56
transfer from contaminated surfaces to the hand upon contact 19–21 and from hands to the mucous
57
membranes on the face 21–23. People touch their faces frequently, with studies reporting average hand-
58
to-face contacts ranging from 16 to 37 times an hour 24–26. Taken together, this suggests surface
59
contamination could pose a risk for indirect SARS-CoV-2 transmission, similar to other respiratory
60
viruses8.
61
62
Despite the potential importance of indirect transmission, it is difficult to estimate its role relative to
63
direct transmission. Quantitative Microbial Risk Analysis (QMRA) provides a framework for
64
understanding health risks from indirect transmission and provides insights into potential impacts of
65
infection control recommendations. Mechanistic models of transmission events within the context of
66
QMRA frameworks have been used to identify risks for a number of scenarios including children
67
playing with fomites 27, sanitation workers collecting and processing urine for nutrient recovery 28,
68
and people sharing a houseboat.29 Within the context of the current COVID-19 pandemic, QMRA has
69
been adapted to evaluate and compare transmission risks for MERS-CoV and SARS-CoV-2 through
70
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
4
droplets, aerosolized particles, and doffing personal protective equipment in hospitals 30–32 and to
71
evaluate the effectiveness masks at reducing the risk of SARS-CoV-2 infection 33.
72
73
In this study, two mechanistic models of indirect transmission within the QMRA framework are used
74
to estimate the risk of infection for SARS-CoV-2 in community settings and inform guidance on
75
potential intervention strategies. Specifically, a model is developed to estimate the risk of infection for
76
single contacts with contaminated surfaces, with the concentrations of SARS-CoV-2 RNA on the
77
surfaces informed by literature investigating surface contamination in public spaces (bus stations, gas
78
stations, stores, playgrounds). A second model is used to estimate risks from surface-mediated
79
community transmission as a function of the prevalence of COVID-19 cases in the community and to
80
test the efficacy of feasible intervention strategies of hand disinfection and surface disinfection.
81
82
Methods
83
Model 1. Risks from contaminated surfaces
84
A stochastic-mechanistic model was developed to estimate the infection risk for a single hand-to-
85
surface followed by hand-to-face contact (Figure S1). The concentration of SARS-CoV-2 RNA on
86
public surfaces [gene copy number (gc) cm-2] was obtained from literature13,15. Conversion of SARS-
87
CoV-2 RNA to infective virus was assumed to follow a uniform distribution with range 100 and 1000
88
(gc per infective virus, with infective virus measured using Plaque Forming Units (PFU)). The
89
gc:PFU ratio is based on the sparsely available information of SARS-CoV-2 found in literature 18,34,35,
90
data from enveloped respiratory RNA viruses36 (seasonal influenza A(H1N1), A(H3N2), and
91
influenza B have mean ratios of 708, 547, and 185 gene copies per TCID50 respectively), and
92
a ratio of 0.7 to convert TCID50 to PFU37. The transfer of virus from surface-to-hand and from hand-
93
to-mucous membranes was assumed to be proportional to the concentration of virus on the surface
94
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
5
and the transfer efficiency of virus at both interfaces38. An exponential dose-response model39 was
95
used to estimate the probability of infection for a given dose. This model is based on the pooled data
96
of studies of SARS-CoV40 and Murine hepatitis virus (MHV-1)41 infection in mice. The upper bounds
97
of the dose-response curve are consistent with the infectivity of two different variants of SARS-CoV-
98
2 in mice, hamsters, and ferrets42. Monte Carlo simulations were used to incorporate the uncertainty
99
and variability of the input parameters. The model was simulated 50,000 times. Results are presented
100
as the median risk values with 5th and 95th percentiles. The equations used, the probability
101
distributions for the input parameters, and a diagram of the model can be found in the Supporting
102
Information (Figure S1-S3, Table S1).
103
104
Model 2. Risks from surface-mediated community transmission
105
Contamination of SARS-CoV-2 on surfaces in public spaces (e.g., traffic light buttons, train buttons)
106
was modeled as a function of disease prevalence in the community and frequency of contact with the
107
surface. Estimates obtained in the model describe the probability of infection for people contacting the
108
surface across a period of seven days. In the model, surface inoculation happens when infected
109
individuals use their hand to cover their mouth while coughing and subsequently touching a surface.
110
Viral loads [gc mL-1] in the saliva or sputum of symptomatic COVID-19 patients within the first 14
111
days of symptom onset were used as input to the model35,43–45. The concentrations of SARS-CoV-2 in
112
saliva and sputum samples measured in genome copies35,43–45, align with concentrations of samples
113
measured in TCID50 34 once they are adjusted by the previously mentioned genome copies to
114
infectivity ratio.
115
116
The frequency of surface contamination was determined by the prevalence of the disease in the
117
population46–50. A cough was assumed to spread particles conically51. Therefore, virus inoculation on
118
hands was modeled as a function of the concentration of virus in the saliva, the volume of saliva
119
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
6
expelled per cough, the distance between the mouth and the hand, and the right circular cone angle of the
120
ejected particles, 𝑎 (Figure S2, Table S1). Transfer from surface-to-hand and from hand-to-mucous
121
membranes was assumed proportional to the concentration of virus on the surface and the transfer
122
efficiency of virus at both interfaces38 (Figure S1). The concentration of virus in the contaminated surface
123
was assumed to decay exponentially 52. Decay rate was obtained from research on SARS-CoV-2 survival
124
on various surfaces 18. An exponential dose-response model39 was used to estimate the probability of
125
infection for a given dose. The concentration on the contaminated surface and on the hand was
126
reduced according to the log10 reduction values for the scenarios of surface and hand disinfection.
127
Alcohol-based hand sanitizer was selected as hand disinfection strategy due to the widespread
128
availability and portability of hand-sanitizers. Although hand-washing was not considered, based on
129
the log reductions of enveloped viruses achieved by handwashing53, we assume effectiveness of
130
handwashing is similar to hand sanitizer for the reduction of SARS-CoV-2 on hands.
131
132
Monte Carlo simulations were used to incorporate uncertainty and variability of the input parameters
133
in the risk characterization. Convergence was tested for the baseline scenario by running five times 5
134
000, 10 000, 20 000, 50 000, and 100 000 simulations. There was minimal variation after 50 000
135
simulations (Supplementary Figure 2). Based on the results, all the models were simulated 50,000
136
times. For each of the 50,000 simulations, the risks were calculated across time, for a period of seven
137
days. Therefore, each simulation has a time profile of contaminations and risks. The median, 25th and
138
75th quartiles of the seven day simulations were recorded for each of the 50,000 simulations and the
139
average values of the median, 25th and 75th quartiles are reported (Figure 2). A sensitivity analysis was
140
performed to investigate how the variability and uncertainty of the parameters in the model influenced
141
the estimated risks. The sensitivity was estimated using the Spearman’s correlation coefficients
142
between the inputs and outputs of the model. A detailed description of the model and model
143
parameters are found in the Supporting Information (Figure S1, Table S1).
144
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
7
Results and discussion:
145
Risks from contaminated surfaces
146
Risks of SARS-CoV-2 infection from contact with a fomite in community settings are estimated to be
147
low (Figure 1) and influenced by both infection prevalence rate in the community and the frequency
148
with which the fomite is contacted (Figure 2). Median risk of infection from interaction with a
149
contaminated fomite is linearly related to surface contamination, ranging from 2x10-8 for a surface
150
with 0.01 RNA genome copies (gc) cm-2 to approximately 1 for a surface with ≥106 RNA gc cm-2
151
(Figure 1). Previous studies of surface contamination on public spaces have detected 0.1 to 102
152
SARS-CoV-2 gc cm-2 13,15. In the two studies only 3 of 1281 (0.2%) surfaces sampled were associated
153
with risks of infection greater than 1 in 10,000. The average risk of infection for the sampled surfaces
154
was of 8.5x10-7, assuming negligible risks for samples with SARS-CoV-2 RNA below the LOD (1203
155
out of 1281 surfaces).
156
157
Figure 1. Risk of SARS-CoV-2 infection (unitless, from 0 to 1) as a function of virus concentration on
158
surfaces (genome copies (gc)/cm2). Median risk of infection is shown in a continuous black line; Gray lines
159
display the 5th and 95th percentiles. Orange circles13 and green diamonds15 represent the median risk estimates
160
for point values of surface contamination in public spaces with whiskers from the 5th to the 95th percentiles. Data
161
from Abrahao et al., orange circles, shows the risk for the 6 quantifiable samples out of the 49 RNA positive
162
samples. Data from Harvey et al., green diamonds, shows the risk of 3 quantifiable samples out of the 29 RNA
163
positive samples.
164
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
8
Risks from surface-mediated community transmission
165
When modeling risks of surface contamination within communities, average median value [IQR] risks
166
range from approximately 1.6x10-4 [2.0x10-5 , 1.4x10-3] for the highest risk scenario (5% infection
167
prevalence rate, object contacted once every 1-20 minutes) to 5.6x10-9 [7.4x10-12, 1.6x10-6] in the
168
lowest risk scenario (0.2% prevalence rate, object contacted once every 1-4 hours) (Figure 2). The
169
overwhelming majority of interactions with fomites modeled were associated with risks < 10-4 (Table
170
S2). The low risks of community transmission of SARS-CoV-2 via fomites is in accordance with
171
previous studies and opinions of fomite-mediated transmission in hospitals4–7.
172
According to the sensitivity analysis, the model parameters most influencing estimated infection risks
173
within a community are transfer efficiency between the surface and the hand, 𝑇𝐸𝑠ℎ, and concentration
174
of SARS-CoV-2 in sputum or saliva, 𝐶𝑠𝑝 (Table S1, Figure S4). 𝑇𝐸𝑠ℎ was inversely correlated with
175
risk (Spearman’s rank correlation, ρ = -0.58) and 𝐶𝑠𝑝 was directly correlated (ρ = 0.29). Correlation
176
was low with all other modeled parameters (ρ < 0.05).
177
178
Effectiveness of hand and surface disinfection
179
Hand hygiene was consistently the most effective intervention. Alcohol-based hand disinfectants are
180
portable, widely available, and effective at inactivating coronavirus 54,55. Even with low compliance,
181
representative of only 1 in 4 people disinfecting hands after surface contact, median infection risks
182
from fomite contact were reduced by 0.6-2.2 log10. Under high compliance, representing 3 of every 4
183
people disinfecting, median risks decreased by 3.8-4.3 log10. Importantly, the impact of hand hygiene
184
also appears to be independent of surface contact frequency and prevalence rates, suggesting a
185
strategy of hand disinfection promotion in community settings is universally applicable. Our findings
186
re-affirm the existing strategies of promoting hand hygiene and making hand disinfect products
187
widely available in shared community settings56.
188
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
9
189
Figure 3. Predicted community-based risk of SARS-CoV-2 infection due to hand-to-surface followed by
190
hand-to-face contact. The plot shows the average median risk of infection, with whiskers from the 25th to the
191
75th percentiles. Two interventions were tested (hand disinfection [green] and surface disinfection [orange]) in
192
parallel to no intervention control [black]. Compliance for hand disinfection was set to 25, 50, and 75% of the
193
population. Surface disinfection regimes were: every day at 7am, 12pm, or 7am and 12pm. The horizontal black
194
dotted line illustrates the median risk of infection without intervention. Two contact frequencies and three
195
prevalence levels (percentage of the population sick at any given time) were modeled: high contact frequency
196
[1-20 min] and low contact frequency [60-240 min] and low [0.2%], medium [1%], and high [5%] prevalence.
197
The risk of infection of 10-6 is equivalent to one person sick as a result of hand-to-mouth contact every million
198
people touching the surface.
199
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
10
Although the risks of SARS-CoV-2 transmission via fomites are estimated to be low, they are
200
possible and may contribute a small number of new cases during outbreaks. For both surfaces with
201
quantified contamination and modeled surfaces within a community, infection risk estimates are very
202
low when people interact with a single fomite. However, a person’s infection risk increases when
203
accounting for the hundreds of objects contacted every hour, and the thousands of frequently
204
contacted objects (crosswalk buttons, public transportation buttons, ATMs, and railings) within a city.
205
Each interaction provides an opportunity for SARS-CoV-2 transmission. Risk of infection from
206
multiple contacts with fomites – as compared to a single contact with a fomite – is substantially
207
higher. Nevertheless, in our models the risk of infection from a fomite is orders of magnitude lower
208
than the prevalence rates, suggesting the relative contribution of fomite-mediated transmission might
209
be small compared to other transmission routes.
210
211
The data used to quantify risks from measured concentrations of SARS-CoV-2 RNA on surfaces in
212
public spaces were obtained from two locations: Somerville, Massachusetts, USA15, and Belo
213
Horizonte, Minas Gerais State, Brazil13. The sampling collection for both studies occurred throughout
214
a COVID-19 outbreak from March-June 2020. Both places had control measures when the collection
215
took place, including mandatory use of masks in public spaces. The mask use requirement may have
216
influenced surface contamination, with the measured SARS-CoV-2 RNA concentrations lower than
217
what could be observed without a mask requirement. Our modeled interventions included hand
218
disinfection and surface disinfection, but given the widespread use of masks within a
219
community, masks may also help to curb fomite-mediated transmission. Masks are repeatedly shown
220
to be effective at reducing transmission of SARS-CoV-257 through the proposed mechanism of
221
limiting both production of and exposure to aerosolized droplets. Masks may also influence fomite-
222
mediated transmission by reducing hand or surface contamination from droplets and/or reducing
223
hand-to-mouth contact frequency. As there is currently insufficient data on the effectiveness of masks
224
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
11
against droplet production and on the frequency of hand-to-mouth contacts, mask use could not be
225
considered as an intervention here.
226
227
The model findings are influenced by the model implementation and assumptions, and changes in
228
assumptions may shift some of our conclusions. First, absolute infection risks from QMRA may be
229
unreliable due to the uncertainty and/or variability in the estimates of the parameters58. The
230
exponential dose-response model in particular suffers from a number of limitations: the model is
231
based on data of SARS-CoV and Murine hepatitis virus (MHV-1) infection in mice by intranasal
232
administration40,41. Extrapolating the model from mice to people and from MHV-1 and SARS-CoV to
233
SARS-CoV-2, introduces uncertainty in infection risk estimates, but – in accordance with current best
234
practice 59 – we did not consider this here. Nevertheless, dose-response relationships derived from
235
animal studies tend to be more conservative60. An additional limitation is that the dose-response
236
relationship was determined using virus as measured in units of Plaque Forming Units (PFU) and
237
therefore a ratio of genome copies to PFU is needed. The assumed range of ratios of 1:100 to 1:1000
238
for genome copies to viable virus is based on Influenza, along with the sparse data currently available
239
for SARS-CoV-2. Data quantifying viable virus on fomites in communities would be the “gold
240
standard”, but detection of viable virus is unlikely given previously observed concentrations of
241
SARS-CoV-2 RNA align with estimates of viable virus of <1 / 100 cm2. Because of the uncertainties
242
in parameter estimates, QMRA estimates of relative risk reduction from interventions are viewed as
243
more reliable because potential biases in data are incorporated into both the intervention and control
244
risk estimates58.
245
246
Additional model characteristics likely influence risk estimates. Model parameters used for virus
247
transfer and decay rates are determined experimentally in laboratory conditions and could be different
248
in environmental conditions. Also, prevalence rates modeled here are assumed to correspond directly
249
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
12
with the percent of people who are infected and contact the surface with a hand contaminated by
250
coughing. In reality, an unknown fraction of infected people would likely either: 1) stay at home (i.e.,
251
quarantine and/or isolation), or 2) not cough directly on their hand. In this regard, the modeled
252
infection risks are likely higher (more conservative) than would be expected at the stated community
253
infection prevalence rates.
254
255
Despite the limitations of the underlying model, Quantitative Microbial Risk Assessment remains a
256
valuable tool to understand and characterize risks of surface-mediated transmission of SARS-CoV-2
257
within communities and test the effectiveness of different interventions. Epidemiological
258
investigations and/or structured experimental designs (i.e., randomized controlled trials) are infeasible
259
given that fomite-mediated transmission is likely a rare event and is difficult to decouple from other –
260
more likely – transmission routes. The results presented here add to the evidence supporting the
261
relatively low contribution of fomites in the transmission of SARS-CoV-215, and can inform guidance
262
on potential intervention strategies.
263
264
265
Acknowledgements
266
We thank Danilo Cuccato, Emmanuel Froustey for their inputs on the model, and Diego Marcos,
267
Sunil K. Dogga, Gabriele Micali, Esther Greenwood, Sital Uprety and Elyse Stachler for reviewing
268
the manuscript. A.K.P. was supported by Swiss National Science Foundation – SNSF.
269
Author contributions
270
T.R.J. and A.K.P designed the study. A.K.P. performed the modeling. T.R.J. and A.K.P wrote the
271
manuscript.
272
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
13
Competing interests
273
We have no competing interests to declare.
274
275
276
References
277
1. Centers for Disease Control (CDC). Scientic Brief: SARS-CoV-2 and Potential Airborne
278
Transmission. 4–7 (2020).
279
2. World Health Organization. Transmission of SARS-CoV-2: implications for infection
280
prevention precautions. Scientific brief. 1–10 (2020).
281
3. Ferretti, L. et al. Quantifying SARS-CoV-2 transmission suggests epidemic control with
282
digital contact tracing. Science (80-. ). 368, 0–8 (2020).
283
4. Goldman, E. Exaggerated risk of transmission of COVID-19 by fomites. Lancet Infect. Dis.
284
20, 892–893 (2020).
285
5. Mondelli, M. U., Colaneri, M., Seminari, E. M., Baldanti, F. & Bruno, R. Low risk of SARS-
286
CoV-2 transmission by fomites in real-life conditions. Lancet Infect. Dis. 3099, 30678 (2020).
287
6. Colaneri, M. et al. Lack of SARS-CoV-2 RNA environmental contamination in a tertiary
288
referral hospital for infectious diseases in Northern Italy. J. Hosp. Infect. 105, 474–476 (2020).
289
7. Colaneri, M. et al. Severe acute respiratory syndrome coronavirus 2 RNA contamination of
290
inanimate surfaces and virus viability in a health care emergency unit. Clin. Microbiol. Infect.
291
26, 1094.e1-1094.e5 (2020).
292
8. Boone, S. A. & Gerba, C. P. Significance of fomites in the spread of respiratory and enteric
293
viral disease. Appl. Environ. Microbiol. 73, 1687–1696 (2007).
294
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
14
9. Kraay, A. N. M. et al. Fomite-mediated transmission as a sufficient pathway: a comparative
295
analysis across three viral pathogens. BMC Infect. Dis. (2018). doi:10.1186/s12879-018-3425-
296
x
297
10. Xiao, S., Li, Y., Wong, T. wai & Hui, D. S. C. Role of fomites in SARS transmission during
298
the largest hospital outbreak in Hong Kong. PLoS One (2017).
299
doi:10.1371/journal.pone.0181558
300
11. Ong, S. W. X. et al. Air, Surface Environmental, and Personal Protective Equipment
301
Contamination by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) from a
302
Symptomatic Patient. JAMA - J. Am. Med. Assoc. 2–4 (2020). doi:10.1001/jama.2020.3227
303
12. Ye, G. et al. Environmental contamination of the SARS-CoV-2 in healthcare premises: An
304
urgent call for protection for healthcare workers. medRxiv Prepr. 1–20 (2020).
305
doi:10.1101/2020.03.11.20034546
306
13. Abrahao, J. S. et al. Detection of SARS-CoV-2 RNA on public surfaces in a densely populated
307
urban area ofBrazil: A potential tool for monitoring the circulation of infected patients. Sci.
308
Total Environ. (2020). doi:10.1016/j.scitotenv.2020.142645
309
14. Chia, P. Y. et al. Detection of air and surface contamination by SARS-CoV-2 in hospital
310
rooms of infected patients. Nat. Commun. 11, (2020).
311
15. Harvey, A. et al. Longitudinal monitoring of SARS-CoV-2 RNA on high-touch surfaces in a
312
community setting. Submitted (2020).
313
16. Riddell, S., Goldie, S., Hill, A., Eagles, D. & Drew, T. W. The effect of temperature on
314
persistence of SARS-CoV-2 on common surfaces. Virol. J. 17, 145 (2020).
315
17. Chin, A. et al. Stability of SARS-CoV-2 in different environmental conditions. Lancet
316
Microbe 5247, 2020.03.15.20036673 (2020).
317
18. van Doremalen, N. et al. Aerosol and Surface Stability of SARS-CoV-2 as Compared with
318
SARS-CoV-1. N. Engl. J. Med. 1–3 (2020). doi:10.1056/NEJMc2004973
319
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
15
19. Julian, T. R., Leckie, J. O. & Boehm, A. B. Virus transfer between fingerpads and fomites. J.
320
Appl. Microbiol. 109, 1868–1874 (2010).
321
20. Lopez, G. U. et al. Transfer efficiency of bacteria and viruses from porous and nonporous
322
fomites to fingers under different relative humidity conditions. Appl. Environ. Microbiol. 79,
323
5728–5734 (2013).
324
21. Rusin, P., Maxwell, S. & Gerba, C. Comparative surface-to-hand and fingertip-to-mouth
325
transfer efficiency of gram-positive bacteria, gram-negative bacteria, and phage. J. Appl.
326
Microbiol. 93, 585–592 (2002).
327
22. Pitol, A. K., Bischel, H., Kohn, T. & Julian, T. R. Virus transfer at the skin-liquid interface.
328
Environ. Sci. Technol. 51, 14417–14425 (2017).
329
23. Lu, C. wei, Liu, X. fen & Jia, Z. fang. 2019-nCoV transmission through the ocular surface
330
must not be ignored. The Lancet (2020). doi:10.1016/S0140-6736(20)30313-5
331
24. Nicas, M. & Best, D. A study quantifying the hand-to-face contact rate and its potential
332
application to predicting respiratory tract infection. J. Occup. Environ. Hyg. 5, 347–352
333
(2008).
334
25. Kwok, Y. L. A., Gralton, J. & McLaws, M. L. Face touching: A frequent habit that has
335
implications for hand hygiene. Am. J. Infect. Control 43, 112–114 (2015).
336
26. Lewis, R. C., Rauschenberger, R. & Kalmes, R. Hand-to-mouth and other hand-to-face
337
touching behavior in a quasi-naturalistic study under controlled conditions ABSTRACT. J.
338
Toxicol. Environ. Heal. Part A 00, 1–7 (2020).
339
27. Julian, T. R., Canales, R. a., Leckie, J. O. & Boehm, A. B. A model of exposure to rotavirus
340
from nondietary ingestion iterated by simulated intermittent contacts. Risk Anal. 29, 617–632
341
(2009).
342
28. Bischel, H. N., Caduff, L., Schindelholz, S., Kohn, T. & Julian, T. R. Health Risks for
343
Sanitation Service Workers along a Container-Based Urine Collection System and Resource
344
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
16
Recovery Value Chain. Environ. Sci. Technol. 53, 7055–7067 (2019).
345
29. Canales, R. A. et al. Modeling the role of fomites in a norovirus outbreak. J. Occup. Environ.
346
Hyg. 16, 16–26 (2019).
347
30. Adhikari, U. et al. A Case Study Evaluating the Risk of Infection from Middle Eastern
348
Respiratory Syndrome Coronavirus (MERS-CoV) in a Hospital Setting Through Bioaerosols.
349
Risk Anal. 39, 2608–2624 (2019).
350
31. King, M.-F. et al. Modelling the risk of SARS-CoV-2 infection through PPE doffing in a
351
hospital environment. medRxiv 2020.09.20.20197368 (2020).
352
doi:10.1101/2020.09.20.20197368
353
32. Jones, R. M. Relative contributions of transmission routes for COVID-19 among healthcare
354
personnel providing patient care. J. Occup. Environ. Hyg. 0, 1–8 (2020).
355
33. Wilson, A. M. et al. COVID-19 and use of non-traditional masks: how do various materials
356
compare in reducing the risk of infection for mask wearers? J. Hosp. Infect. (2020).
357
34. Bullard, J. et al. Predicting Infectious Severe Acute Respiratory Syndrome Coronavirus 2
358
From Diagnostic Samples. Clin. Infect. Dis. 1–4 (2020). doi:10.1093/cid/ciaa638
359
35. Kim, J. Y. et al. Viral load kinetics of SARS-CoV-2 infection in first two patients in Korea. J.
360
Korean Med. Sci. 35, 1–7 (2020).
361
36. Ip, D. K. M. et al. The Dynamic Relationship between Clinical Symptomatology and Viral
362
Shedding in Naturally Acquired Seasonal and Pandemic Influenza Virus Infections. Clin.
363
Infect. Dis. 62, 431–437 (2015).
364
37. ATCC. Converting TCID50 to plaque forming units PFU-124. 1 (2012). Available at:
365
https://www.lgcstandards-
366
atcc.org/Global/FAQs/4/8/Converting_TCID50_to_plaque_forming_units_PFU-
367
124.aspx?geo_country=gb#. (Accessed: 20th October 2020)
368
38. Wilson, A. M. et al. Evaluating a transfer gradient assumption in a fomite-mediated microbial
369
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
17
transmission model using an experimental and Bayesian approach. J. R. Soc. Interface 17,
370
(2020).
371
39. Watanabe, T., Bartrand, T. A., Weir, M. H., Omura, T. & Haas, C. N. Development of a Dose-
372
Response Model for SARS Coronavirus. 30, (2010).
373
40. DeDiego, M. L. et al. Pathogenicity of severe acute respiratory coronavirus deletion mutants in
374
hACE-2 transgenic mice. Virology (2008). doi:10.1016/j.virol.2008.03.005
375
41. De Albuquerque, N. et al. MurineHepatitis Virus Strain 1 Produces a Clinically Relevant
376
Model of Severe Acute Respiratory Syndrome in A/J Mice. J. Virol. (2006).
377
doi:10.1128/jvi.00747-06
378
42. Zhou, B. et al. SARS-CoV-2 spike D614G variant confers enhanced replication and
379
transmissibility. bioRxiv (2020). doi:https://doi.org/10.1101/2020.10.27.357558
380
43. Wölfel, R. et al. Virological assessment of hospitalized patients with COVID-2019. Nature 1–
381
14 (2020). doi:10.1038/s41586-020-2196-x
382
44. Pan, Y., Zhang, D., Yang, P., Poon, L. L. M. & Wang, Q. Viral load of SARS-CoV-2 in
383
clinical samples. Lancet Infect. Dis. 20, 411–412 (2020).
384
45. To, K. K. W. et al. Temporal profiles of viral load in posterior oropharyngeal saliva samples
385
and serum antibody responses during infection by SARS-CoV-2: an observational cohort
386
study. Lancet Infect. Dis. 20, 565–574 (2020).
387
46. Bendavid, E. et al. COVID-19 Antibody Seroprevalence in Santa Clara County, California.
388
medRxiv 2020.04.14.20062463 (2020). doi:10.1101/2020.04.14.20062463
389
47. Perez-Saez, J. et al. Serology-informed estimates of SARS-CoV-2 infection fatality risk in
390
Geneva, Switzerland. Lancet Infect. Dis. 3099, 2–3 (2020).
391
48. Pollán, M. et al. Prevalence of SARS-CoV-2 in Spain (ENE-COVID): a nationwide,
392
population-based seroepidemiological study. Lancet 396, 535–544 (2020).
393
49. Erikstrup, C. et al. Estimation of SARS-CoV-2 infection fatality rate by real-time antibody
394
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
18
screening of blood donors. Clin. Infect. Dis. (2020). doi:10.1093/cid/ciaa849
395
50. Amorim Filho, L. et al. Seroprevalence of anti-SARS-CoV-2 among blood donors in Rio de
396
Janeiro, Brazil. Rev. Saude Publica 54, 69 (2020).
397
51. Nicas, M. & Sun, G. An integrated model of infection risk in a health-care environment. Risk
398
Anal. 26, 1085–1096 (2006).
399
52. van Doremalen, N. et al. Aerosol and Surface Stability of SARS-CoV-2 as Compared with
400
SARS-CoV-1. N. Engl. J. Med. (2020). doi:10.1056/NEJMc2004973
401
53. Narendra Kumar Chaudhary et al. Fighting the SARS CoV-2 (COVID-19) Pandemic with
402
Soap. Preprints 060, 1–19 (2020).
403
54. Rabenau, H. F., Kampf, G., Cinatl, J. & Doerr, H. W. Efficacy of various disinfectants against
404
SARS coronavirus. J. Hosp. Infect. 61, 107–111 (2005).
405
55. Golin, A. P., Choi, D. & Ghahary, A. Hand sanitizers: A review of ingredients, mechanisms of
406
action, modes of delivery, and efficacy against coronaviruses. Am. J. Infect. Control (2020).
407
doi:10.1016/j.ajic.2020.06.182
408
56. World Health Organization (WHO). Recommendation to Member States to improve hand
409
hygiene practices widely to help prevent the transmission of the COVID-19 virus. Interim
410
guidance (2020).
411
57. Liang, M. et al. Efficacy of face mask in preventing respiratory virus transmission: A
412
systematic review and meta-analysis. Travel Med. Infect. Dis. 101751 (2020).
413
doi:10.1016/j.tmaid.2020.101751
414
58. World Health Organization (WHO). Quantitative microbial risk assessment. Application for
415
water safety management. (2016).
416
59. Haas, C. N., Rose, J. B. & Gerba, C. P. Quantitative Microbial Risk Assessment: Second
417
Edition. John Wiley & Sons, Inc. (2014). doi:10.1002/9781118910030
418
60. Haas, C. WikiQMRA: Completed Dose Response Models. Available at:
419
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
19
http://qmrawiki.org/framework/dose-response/experiments. (Accessed: 1st October 2020)
420
421
422
423
424
425
Table of Contents (TOC)
426
427
For Table of Contents Only
428
429
. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted November 23, 2020. ; https://doi.org/10.1101/2020.11.20.20220749doi: medRxiv preprint
