Qifang Bi’s research while affiliated with Johns Hopkins Bloomberg School of Public Health and other places

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Publications (30)


Fig. 1 Epidemic curve and recruitment period of household serosurvey. a daily confirmed COVID-19 cases reported in Geneva up to July 1st, 2020. b Daily number of recruited households over the 12-week study period. First detected case in Geneva canton was reported on February 26th, and the first epidemic wave lasted about two months. Yellow bands indicate time
Insights into household transmission of SARS-CoV-2 from a population-based serological survey
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December 2021

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266 Reads

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79 Citations

Qifang Bi

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Isabella Eckerle

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Didier Trono

Understanding the risk of infection from household- and community-exposures and the transmissibility of asymptomatic infections is critical to SARS-CoV-2 control. Limited previous evidence is based primarily on virologic testing, which disproportionately misses mild and asymptomatic infections. Serologic measures are more likely to capture all previously infected individuals. We apply household transmission models to data from a cross-sectional, household-based population serosurvey of 4,534 people ≥5 years from 2,267 households enrolled April-June 2020 in Geneva, Switzerland. We found that the risk of infection from exposure to a single infected household member aged ≥5 years (17.3%,13.7-21.7) was more than three-times that of extra-household exposures over the first pandemic wave (5.1%,4.5-5.8). Young children had a lower risk of infection from household members. Working-age adults had the highest extra-household infection risk. Seropositive asymptomatic household members had 69.4% lower odds (95%CrI,31.8-88.8%) of infecting another household member compared to those reporting symptoms, accounting for 14.5% (95%CrI, 7.2-22.7%) of all household infections.

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Figure 1: Timing of serological testing and seropositive results relative to last putative exposure Time of serological testing from last putative exposure to an index case, among all PCR-negative close contacts (A) and among those with seropositive results (B). All close contacts had one serological test each. The Shenzhen cohort was defined as individuals who were included in a previous study that characterised the epidemiology and transmission of COVID-19 in Shenzhen, by Bi and colleagues. 8
Insight into the practical performance of RT-PCR testing for SARS-CoV-2 using serological data: a cohort study

January 2021

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130 Reads

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84 Citations

The Lancet Microbe

Background Virological detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through RT-PCR has limitations for surveillance. Serological tests can be an important complementary approach. We aimed to assess the practical performance of RT-PCR-based surveillance protocols and determine the extent of undetected SARS-CoV-2 infection in Shenzhen, China. Methods We did a cohort study in Shenzhen, China and attempted to recruit by telephone all RT-PCR-negative close contacts (defined as those who lived in the same residence as, or shared a meal, travelled, or socially interacted with, an index case within 2 days before symptom onset) of all RT-PCR-confirmed cases of SARS-CoV-2 detected since January, 2020, via contact tracing. We measured anti-SARS-CoV-2 antibodies in serum samples from RT-PCR-negative close contacts 2–15 weeks after initial virological testing by RT-PCR, using total antibody, IgG, and IgM ELISAs. In addition, we did a serosurvey of volunteers from neighbourhoods with no reported cases, and from neighbourhoods with reported cases. We assessed rates of infection undetected by RT-PCR, performance of RT-PCR over the course of infection, and characteristics of individuals who were seropositive on total antibody ELISA but RT-PCR negative. Findings Between April 12 and May 4, 2020, we enrolled and collected serological samples from 2345 (53·0%) of 4422 RT-PCR-negative close contacts of cases of RT-PCR-confirmed SARS-CoV-2. 1175 (50·1%) of 2345 were close contacts of cases diagnosed in Shenzhen with contact tracing details, and of these, 880 (74·9%) had serum samples collected more than 2 weeks after exposure to an index case and were included in our analysis. 40 (4·5%) of 880 RT-PCR-negative close contacts were positive on total antibody ELISA. The seropositivity rate with total antibody ELISA among RT-PCR-negative close contacts, adjusted for assay performance, was 4·1% (95% CI 2·9–5·7), which was significantly higher than among individuals residing in neighbourhoods with no reported cases (0·0% [95% CI 0·0–1·1]). RT-PCR-positive individuals were 8·0 times (95% CI 5·3–12·7) more likely to report symptoms than those who were RT-PCR-negative but seropositive, but both groups had a similar distribution of sex, age, contact frequency, and mode of contact. RT-PCR did not detect 48 (36% [95% CI 28–44]) of 134 infected close contacts, and false-negative rates appeared to be associated with stage of infection. Interpretation Even rigorous RT-PCR testing protocols might miss a substantial proportion of SARS-CoV-2 infections, perhaps in part due to difficulties in determining the timing of testing in asymptomatic individuals for optimal sensitivity. RT-PCR-based surveillance and control protocols that include rapid contact tracing, universal RT-PCR testing, and mandatory 2-week quarantine were, nevertheless, able to contain community spread in Shenzhen, China. Funding The Bill & Melinda Gates Foundation, Special Foundation of Science and Technology Innovation Strategy of Guangdong Province, and Key Project of Shenzhen Science and Technology Innovation Commission.


Figure 2. Characteristics associated with timing of dengue seasonal epidemic in Thailand. 269 Association of seasonal epidemic time lag with (a) the population density of a district, (b) the 270 distance between a district and its regional urban center, defined as the district with the highest 271 population density in a health region, and (c) the distance between central Bangkok and a 272 district within a 200km radius from central Bangkok. The seasonal epidemic time lag is defined 273 as the number of days the intra-annual DHF/DSS waves in a district preceded (positive 274 numbers) or lagged (negative numbers) the aggregated country-level DHF/DSS waves. Each 275
Figure 3. Relationship between timing of dengue seasonal epidemics, fadeout, and seasonal 286 forcing in a simulated two-patch model. a, Stochastic phase differences of seasonal epidemics 287 on annual scale between the urban and rural patch for various parameter combinations. í µí»¼ í µí±¢ = 288 0.2, í µí»¼ í µí±Ÿ = 0.1, 0.2, 0.4, 0.8, and 0<m<1; Positive numbers indicate the number of days the intra-289 annual waves in the rural district preceded that in the urban district, whereas negative numbers 290 indicate a lag. We ran 50 simulations for each combination of parameters for up to 60 years and 291 calculated average phase difference between the simulated seasonal epidemics (0.8 -1.2 292 years) in the urban and rural patch. Black curves represent the smoothed median value of the 293
Seasonal patterns of dengue incidence in Thailand across the urban-rural gradient

November 2020

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95 Reads

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1 Citation

In Southeast Asia, endemic dengue follows strong spatio-temporal patterns with major epidemics occurring every 2-5 years. However, important spatio-temporal variation in seasonal dengue epidemics remains poorly understood. Using 13 years (2003-2015) of dengue surveillance data from 926 districts in Thailand and wavelet analysis, we show that rural epidemics lead urban epidemics within a dengue season, both nationally and within health regions. However, local dengue fade-outs are more likely in rural areas than in urban areas during the off season, suggesting rural areas are not the source of viral dispersion. Simple dynamic models show that stronger seasonal forcing in rural areas could explain the inconsistency between earlier rural epidemics and dengue “over wintering” in urban areas. These results add important nuance to earlier work showing the importance of urban areas in driving multi-annual patterns of dengue incidence in Thailand. Feedback between geographically linked locations with markedly different ecology is key to explaining full disease dynamics across urban-rural gradient.


Figure 1. Epidemic curve and recruitment period of household serosurvey. (A) daily confirmed COVID-1 cases reported in Geneva up to July 1st, 2020. (B) Daily number of recruited households over the 12-we study period. First detected case in Geneva canton was reported on February 26th and the epidemic las about two months. Yellow bands indicate time periods of enrollment for each week. This includes all 4,43 households enrolled in the SEROCoV-POP study, not restricted to the complete households used in the analyses for which serostatus of all household members were available 19 week lasted ,438 hese
Figure 2: Median probability of (A) extra-housheold infection over the duration of the outbreak and (B) in from a single infected household member by age group and sex of the susceptibles. Bars represent 95% credible intervals. Probabilities of being infected by sex and age group of the exposed individuals are estimated by a model only including age and sex of the exposed individuals (model 2, orange/green bars Table S2). The probabilities of being infected by the age group of the exposed individuals combining ma and females (left four grey bars on both panels) are estimated with an age-only model (model 1). The ov probabilities of being infected (rightmost grey bar on both panels) are estimated with the null model (mod
Figure S5. Median probability of (A) extra-housheold infection over the duration of the outbreak and (B) infection from a single infected household member by age group and sex of the susceptibles. Bars repre 95% credible intervals.
Attributable fraction of extra-household infections, within household infections by symptomatics, and within household infections by asymptomatics.
Household Transmission of SARS-COV-2: Insights from a Population-based Serological Survey

November 2020

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153 Reads

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23 Citations

Importance Knowing the transmissibility of asymptomatic infections and risk of infection from household and community exposures is critical to SARS-CoV-2 control. Limited previous evidence is based primarily on virologic testing, which disproportionately misses mild and asymptomatic infections. Serologic measures are more likely to capture all previously infected individuals. Objective Estimate the risk of SARS-CoV-2 infection from household and community exposures, and identify key risk factors for transmission and infection. Design Household serosurvey and transmission model. Setting Population-based serosurvey in Geneva, Switzerland Participants 4,524 household members five years and older from 2,267 households enrolled April-June 2020. Exposures SARS-CoV-2 infected (seropositive) household members and background risk of community transmission. Main outcomes and measures Past SARS-CoV-2 infection confirmed through anti-SARS-CoV-2 IgG antibodies by ELISA. Chain-binomial models based on the number of infections within households were used to estimate extra-household infection risk by demographics and reported extra-household contacts, and infection risk from exposure to an infected household member by demographics and infector’s symptoms. Infections attributable to exposure to different types of infectious individuals were estimated. Results The chance of being infected by a single SARS-CoV-2 infected household member was 17.2% (95%CrI 13.6-21.5%) compared to a cumulative extra-household infection risk of 5.1% (95%CrI 4.5-5.8%). Infection risk from an infected household member increased with age, from 7.5% (95%CrI 1.3-20.3%) among 5-9 years to 30.2% (95%CrI 14.3-48.2%) among those ≥65 years. Working-age adults (20-49 years) had the highest extra-household infection risk. Seropositive household members not reporting symptoms had 74.8% lower odds (95%CrI 43.8-90.3%) of infecting another household member compared to those reporting symptoms, accounting for 19.6% (95%CrI 12.9-24.5%) of all household infections. Conclusions and Relevance The risk of infection from exposure to a single infected household member was four-times that of extra-household exposures over the first wave of the pandemic. Young children had a lower risk from infection from household members. Asymptomatic infections are far less likely to transmit than symptomatic ones but do cause infections. While the small households in Geneva limit the contribution of household spread, household transmission likely plays a greater role in other settings.


A multi-family cluster of COVID-19 associated with asymptomatic and pre-symptomatic transmission in Jixi City, Heilongjiang, China, 2020

October 2020

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382 Reads

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17 Citations

We investigated a multi-family cluster of 22 cases in Jixi, where pre-symptomatic and asymptomatic transmission resulted in at least 41% of household infections of SARS-CoV-2. Our study illustrates the challenge of controlling COVID-19 due to the presence of asymptomatic and pre-symptomatic transmission even when extensive testing and contact tracing are conducted.


Insights into the practical effectiveness of RT-PCR testing for SARS-CoV-2 from serologic data, a cohort study

September 2020

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134 Reads

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5 Citations

Background: Virologic detection of SARS-CoV-2 through Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) has limitations for surveillance. Serologic tests can be an important complementary approach. Objective: Assess the practical performance of RT-PCR based surveillance protocols, and the extent of undetected SARS-CoV-2 transmission in Shenzhen, China. Design: Cohort study nested in a public health response. Setting: Shenzhen, China; January-May 2020. Participants: 880 PCR-negative close-contacts of confirmed COVID-19 cases and 400 residents without known exposure (main analysis). Fifty-seven PCR-positive case contacts (timing analysis). Measurements: Virological testing by RT-PCR. Measurement of anti-SARS-CoV-2 antibodies in PCR-negative contacts 2-15 weeks after initial testing using total Ab ELISA. Rates of undetected infection, performance of RT-PCR over the course of infection, and characteristics of seropositive but PCR-negative individuals were assessed. Results: The adjusted seropositivity rate for total Ab among 880 PCR-negative close-contacts was 4.1% (95%CI, 2.9% to 5.7%), significantly higher than among residents without known exposure to cases (0.0%, 95%CI, 0.0% to 1.0%). PCR-positive cases were 8.0 times (RR; 95% CI, 5.3 to 12.7) more likely to report symptoms than the PCR-negative individuals who were seropositive, but otherwise similar. RT-PCR missed 36% (95%CI, 28% to 44%) of infected close-contacts, and false negative rates appear to be highly dependent on stage of infection. Limitations: No serological data were available on PCR-positive cases. Sample size was limited, and only 20% of PCR-negative contacts met inclusion criteria. Conclusion: Even rigorous RT-PCR testing protocols may miss a significant proportion of infections, perhaps in part due to difficulties timing testing of asymptomatics for optimal sensitivity. Surveillance and control protocols relying on RT-PCR were, nevertheless, able to contain community spread in Shenzhen.


Characterization of clinical progression of COVID-19 patients in Shenzhen, China

April 2020

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551 Reads

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15 Citations

Background Understanding clinical progression of COVID-19 is a key public health priority that informs resource allocation during an emergency. We characterized clinical progression of COVID-19 and determined important predictors for faster clinical progression to key clinical events and longer use of medical resources. Methods and Findings The study is a single-center, observational study with prospectively collected data from all 420 patients diagnosed with COVID-19 and hospitalized in Shenzhen between January 11 th and March 10 th , 2020 regardless of clinical severity. Using competing risk regressions according to the methods of Fine and Gray, we found that males had faster clinical progression than females in the older age group and the difference could not be explained by difference in baseline conditions or smoking history. We estimated the proportion of cases in each severity stage over 80 days following symptom onset using a nonparametric method built upon estimated cumulative incidence of key clinical events. Based on random survival forest models, we stratified cases into risk sets with very different clinical trajectories. Those who progressed to the severe stage (22%,93/420), developed acute respiratory distress syndrome (9%,39/420), and were admitted to the intensive care unit (5%,19/420) progressed on average 9.5 days (95%CI 8.7,10.3), 11.0 days (95%CI 9.7,12.3), and 10.5 days (95%CI 8.2,13.3), respectively, after symptom onset. We estimated that patients who were admitted to ICUs remained there for an average of 34.4 days (95%CI 24.1,43.2). The median length of hospital stay was 21.3 days (95%CI, 20.5,22.2) for cases who did not progress to the severe stage, but increased to 52.1 days (95%CI, 43.3,59.5) for those who required critical care. Conclusions Our analyses provide insights into clinical progression of cases starting early in the course of infection. Patient characteristics near symptom onset both with and without lab parameters have tremendous potential for predicting clinical progression and informing strategic response.


ZIP Code-Level Estimates from a Local Health Survey: Added Value and Limitations

April 2020

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29 Reads

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5 Citations

Journal of Urban Health

We assessed the added value and limitations of generating directly estimated ZIP Code-level estimates by aggregating 5 years of data from an annual cross-sectional survey, the New York City Community Health Survey (n = 44,886) from 2009 to 2013, that were designed to provide reliable estimates only of larger geographies. Survey weights generated directly-observed ZIP Code (n = 128) level estimates. We assessed the heterogeneity of ZIP Code-level estimates within coarser United Hospital Fund (UHF) neighborhood areas (n = 34) by using the Rao-Scott Chi-Square test and one-way ANOVA. Orthogonal linear contrasts assessed whether there were linear trends at the UHF level from 2009 to 2013. 22 of 37 health indicators were reliable in over 50% of ZIP Codes. 14 of the 22 variables showed heterogeneity in ≥4 UHFs. Variables for drinking, nutrition, and HIV testing showed heterogeneity in the most UHFs (9–24 UHFs). In half of the 32 UHFs, >20% variables had within-UHF heterogeneity. Flu vaccination and sugary beverage consumption showed significant time trends in the largest number of UHFs (12 or more UHFs). Overall, heterogeneity of ZIP Code-level estimates suggests that there is value in aggregating 5 years of data to make direct small area estimates.


Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study

April 2020

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730 Reads

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1,898 Citations

The Lancet Infectious Diseases

Background Rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, China, prompted heightened surveillance in Shenzhen, China. The resulting data provide a rare opportunity to measure key metrics of disease course, transmission, and the impact of control measures. Methods From Jan 14 to Feb 12, 2020, the Shenzhen Center for Disease Control and Prevention identified 391 SARS-CoV-2 cases and 1286 close contacts. We compared cases identified through symptomatic surveillance and contact tracing, and estimated the time from symptom onset to confirmation, isolation, and admission to hospital. We estimated metrics of disease transmission and analysed factors influencing transmission risk. Findings Cases were older than the general population (mean age 45 years) and balanced between males (n=187) and females (n=204). 356 (91%) of 391 cases had mild or moderate clinical severity at initial assessment. As of Feb 22, 2020, three cases had died and 225 had recovered (median time to recovery 21 days; 95% CI 20–22). Cases were isolated on average 4·6 days (95% CI 4·1–5·0) after developing symptoms; contact tracing reduced this by 1·9 days (95% CI 1·1–2·7). Household contacts and those travelling with a case were at higher risk of infection (odds ratio 6·27 [95% CI 1·49–26·33] for household contacts and 7·06 [1·43–34·91] for those travelling with a case) than other close contacts. The household secondary attack rate was 11·2% (95% CI 9·1–13·8), and children were as likely to be infected as adults (infection rate 7·4% in children <10 years vs population average of 6·6%). The observed reproductive number (R) was 0·4 (95% CI 0·3–0·5), with a mean serial interval of 6·3 days (95% CI 5·2–7·6). Interpretation Our data on cases as well as their infected and uninfected close contacts provide key insights into the epidemiology of SARS-CoV-2. This analysis shows that isolation and contact tracing reduce the time during which cases are infectious in the community, thereby reducing the R. The overall impact of isolation and contact tracing, however, is uncertain and highly dependent on the number of asymptomatic cases. Moreover, children are at a similar risk of infection to the general population, although less likely to have severe symptoms; hence they should be considered in analyses of transmission and control. Funding Emergency Response Program of Harbin Institute of Technology, Emergency Response Program of Peng Cheng Laboratory, US Centers for Disease Control and Prevention.


The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application

March 2020

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6,798 Reads

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6,729 Citations

Annals of Internal Medicine

Background: A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities. Objective: To estimate the length of the incubation period of COVID-19 and describe its public health implications. Design: Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020. Setting: News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China. Participants: Persons with confirmed SARS-CoV-2 infection outside Hubei province, China. Measurements: Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization. Results: There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine. Limitation: Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases. Conclusion: This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases. Primary funding source: U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.


Citations (21)


... The predicted infectious (1/γ h ) period from our model is about 12 days that was estimated from S-L-V and Marquises islands ( Table 2). These predictions are consistent with some previously measured laboratory data 29,30 . www.nature.com/scientificreports ...

Reference:

Modeling Zika Virus Transmission Dynamics: Parameter Estimates, Disease Characteristics, and Prevention
Times to Key Events in the Course of Zika Infection and their Implications for Surveillance: A Systematic Review and Pooled Analysis
  • Citing Preprint
  • March 2016

... Households are the epicenter of community transmission of acute respiratory viruses, such as Influenza or SARS-CoV-2, with transmission rates resulting from close, repeated and intergenerational interactions [1,3,45]. For instance, in the case of pre-Omicron SARS-CoV-2 virus, the household attack rate, defined as the fraction of infected individuals after infection of an index case, is close to 40%, albeit with large variations across studies reflecting the heterogeneity in pre-existing immunity, social habits, as well as size and composition of households [4,6,7,19]. ...

Insights into household transmission of SARS-CoV-2 from a population-based serological survey

... 4,5 The SARS-CoV-2 literature demonstrated how critical it was to adjust for the varying performance of these laboratory tests (sensitivity and specificity) to draw an accurate inference on prevalence and incidence. 6,7 Indeed, for cross-sectional studies designed to estimate prevalence, the "standard correction" method based on Bayes' rules 6,8 was often applied to account for the laboratory test performance. [9][10][11][12][13] Both frequentist 8,9,14 and Bayesian approaches [11][12][13]15 were proposed for this purpose. ...

Insight into the practical performance of RT-PCR testing for SARS-CoV-2 using serological data: a cohort study

The Lancet Microbe

... Mosquito larvae graze on aquatic microbial growth, which may vary considerably in quality and is driven largely by autochthonous inputs of terrestrial leaf litter detritus [22]. In areas where electricity and water supplies are unreliable, households may store water, increasing the likelihood of colonization by Aedes mosquitoes [23]. Due to variations in the reliability of these municipal services, the presence of artificial container habitats, and human population density, in the example of Vietnam the risk of exposure to DENV varies with human land-use gradients [24], socioeconomic gradients [25], and the built environment [26]. ...

Seasonal patterns of dengue incidence in Thailand across the urban-rural gradient

... Incubation period 5 days [45,46] Proportion of mild infections 80% [47,48] Duration of mild infections 6 days Time from symptoms to hospitalisation [49,50] This information produces response Y t (observable variables) related to the epidemiological or economic effects of the pandemic. Its internal state Θ t (t = 1 . . . ...

The incubation period of 2019-nCoV from publicly reported confirmed cases: estimation and application

... Growing evidence suggests that asymptomatic carriers of the SARS-CoV-2 can also transmit the virus (25,26). It is a challenge to control the disease's spread as asymptomatic individuals are more likely to be out rather than be isolated in their homes, which can pose a significant public health risk (27). Therefore, continual precautions should also be taken to prevent viral transmission. ...

Household Transmission of SARS-COV-2: Insights from a Population-based Serological Survey

... SARS-CoV, MERS-CoV, and SARS-CoV-2 are examples of human coronaviruses that have developed defence mechanisms to block or inhibit the production of interferon, which can occasionally cause host inflammatory reactions resulting in ARDS (4). Suffering from SARS-CoV-2, carriers play a significant role in the transmission of COVID-19, although at least 41% of household infections of SARS-CoV-2 were caused by pre-symptomatic and asymptomatic transmission (5). High transmissibility, international travel, and population density all enhanced the pandemic's global spread. ...

A multi-family cluster of COVID-19 associated with asymptomatic and pre-symptomatic transmission in Jixi City, Heilongjiang, China, 2020

... Misclassification may also exist when a study uses real-time polymerase chain reaction (RT-PCR) but tests contacts too early or too late after exposure, resulting in a low yield in test positivity (an example under Fig. 10). For example, RT-PCR missed 36% (95% CI: 28%, 44%) of infected close-contacts, especially among those who were tested in the first few days after exposure [71]. ...

Insights into the practical effectiveness of RT-PCR testing for SARS-CoV-2 from serologic data, a cohort study

... Similarly, a retrospective analysis in Guangzhou, China found that the secondary transmission rate is equal to 12.4% and 17% among close relative households and people in the same living place, respectively [21]. Another similar study in China evaluated 1286 close contacts and the estimated transmission rate was 11.2% [22]. ...

Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study

The Lancet Infectious Diseases

... Specifically, a statistically significant increased risk of severe COVID-19 disease was found for persons with prior or current TB in only one study with a sample of 36 cases among the 7 studies that examined this association [28]. We speculate that (1) this detected association may not be real; instead it is an artifact of some selection in the sample or (2) the differences in exposure classification preclude generalizable associations between TB and COVID-19 outcomes: 2 of the studies only investigated prior TB as a risk factor [26,31], 3 did not distinguish between prior TB and TB concurrent with COVID-19 diagnosis [25,29,30], and 1 examined concurrent TB, differentiating between TB diagnosed during COVID-19 and prior to hospitalization [21]. These differences highlight the challenge of conducting a formal meta-analysis, or drawing even informal inference across studies. ...

Characterization of clinical progression of COVID-19 patients in Shenzhen, China