Chopping the tail: how preventing superspreading can help to maintain COVID-19 control

AbstractDisease transmission is notoriously heterogeneous, and SARS-CoV-2 is no exception. A skewed distribution where few individuals or events are responsible for the majority of transmission can result in explosive, superspreading events, which produce rapid and volatile epidemic dynamics, especially early or late in epidemics. Anticipating and preventing superspreading events can produce large reductions in overall transmission rates. Here, we present a compartmental (SEIR) epidemiological model framework for estimating transmission parameters from multiple imperfectly observed data streams, including reported cases, deaths, and mobile phone-based mobility that incorporates individual-level heterogeneity in transmission using previous estimates for SARS-CoV-1 and SARS-CoV-2. We parameterize the model for COVID-19 epidemic dynamics by estimating a time-varying transmission rate that incorporates the impact of non-pharmaceutical intervention strategies that change over time, in five epidemiologically distinct settings—Los Angeles and Santa Clara Counties, California; Seattle (King County), Washington; Atlanta (Dekalb and Fulton Counties), Georgia; and Miami (Miami-Dade County), Florida. We find the effective reproduction number ℛE dropped below 1 rapidly following social distancing orders in mid-March, 2020 and remained there into June in Santa Clara County and Seattle, but climbed above 1 in late May in Los Angeles, Miami, and Atlanta, and has trended upward in all locations since April. With the fitted model, we ask: how does truncating the tail of the individual-level transmission rate distribution affect epidemic dynamics and control? We find interventions that truncate the transmission rate distribution while partially relaxing social distancing are broadly effective, with impacts on epidemic growth on par with the strongest population-wide social distancing observed in April, 2020. Given that social distancing interventions will be needed to maintain epidemic control until a vaccine becomes widely available, “chopping off the tail” to reduce the probability of superspreading events presents a promising option to alleviate the need for extreme general social distancing.

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Estimating asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan: a mathematical modeling study

BMC Infectious Diseases ◽

10.1186/s12879-021-06078-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xi Huo ◽

Jing Chen ◽

Shigui Ruan

Keyword(s):

Disease Transmission ◽

Large Scale ◽

Transmission Rate ◽

Control Strategies ◽

Herd Immunity ◽

Antibody Prevalence ◽

Screening Process ◽

Vaccination Programs ◽

Prevalence Level ◽

The Impact

Abstract Background The COVID-19 outbreak in Wuhan started in December 2019 and was under control by the end of March 2020 with a total of 50,006 confirmed cases by the implementation of a series of nonpharmaceutical interventions (NPIs) including unprecedented lockdown of the city. This study analyzes the complete outbreak data from Wuhan, assesses the impact of these public health interventions, and estimates the asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan. Methods By taking different stages of the outbreak into account, we developed a time-dependent compartmental model to describe the dynamics of disease transmission and case detection and reporting. Model coefficients were parameterized by using the reported cases and following key events and escalated control strategies. Then the model was used to calibrate the complete outbreak data by using the Monte Carlo Markov Chain (MCMC) method. Finally we used the model to estimate asymptomatic and undetected cases and approximate the overall antibody prevalence level. Results We found that the transmission rate between Jan 24 and Feb 1, 2020, was twice as large as that before the lockdown on Jan 23 and 67.6% (95% CI [0.584,0.759]) of detectable infections occurred during this period. Based on the reported estimates that around 20% of infections were asymptomatic and their transmission ability was about 70% of symptomatic ones, we estimated that there were about 14,448 asymptomatic and undetected cases (95% CI [12,364,23,254]), which yields an estimate of a total of 64,454 infected cases (95% CI [62,370,73,260]), and the overall antibody prevalence level in the population of Wuhan was 0.745% (95% CI [0.693%,0.814%]) by March 31, 2020. Conclusions We conclude that the control of the COVID-19 outbreak in Wuhan was achieved via the enforcement of a combination of multiple NPIs: the lockdown on Jan 23, the stay-at-home order on Feb 2, the massive isolation of all symptomatic individuals via newly constructed special shelter hospitals on Feb 6, and the large scale screening process on Feb 18. Our results indicate that the population in Wuhan is far away from establishing herd immunity and provide insights for other affected countries and regions in designing control strategies and planing vaccination programs.

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Harnessing testing strategies and public health measures to avert COVID-19 outbreaks during ocean cruises

Scientific Reports ◽

10.1038/s41598-021-95032-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Gerardo Chowell ◽

Sushma Dahal ◽

Raquel Bono ◽

Kenji Mizumoto

Keyword(s):

Public Health ◽

Cost Effective ◽

Contact Network ◽

Social Distancing ◽

Individual Level ◽

Health Measures ◽

Testing Strategies ◽

Novel Coronavirus ◽

The Individual ◽

The Impact

AbstractTo ensure the safe operation of schools, workplaces, nursing homes, and other businesses during COVID-19 pandemic there is an urgent need to develop cost-effective public health strategies. Here we focus on the cruise industry which was hit early by the COVID-19 pandemic, with more than 40 cruise ships reporting COVID-19 infections. We apply mathematical modeling to assess the impact of testing strategies together with social distancing protocols on the spread of the novel coronavirus during ocean cruises using an individual-level stochastic model of the transmission dynamics of COVID-19. We model the contact network, the potential importation of cases arising during shore excursions, the temporal course of infectivity at the individual level, the effects of social distancing strategies, different testing scenarios characterized by the test’s sensitivity profile, and testing frequency. Our findings indicate that PCR testing at embarkation and daily testing of all individuals aboard, together with increased social distancing and other public health measures, should allow for rapid detection and isolation of COVID-19 infections and dramatically reducing the probability of onboard COVID-19 community spread. In contrast, relying only on PCR testing at embarkation would not be sufficient to avert outbreaks, even when implementing substantial levels of social distancing measures.

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Modeling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil

10.1101/2021.10.22.21264706 ◽

2021 ◽

Author(s):

Marcelo Eduardo Borges ◽

Leonardo Souto Ferreira ◽

Silas Poloni ◽

Ângela Maria Bagattini ◽

Caroline Franco ◽

...

Keyword(s):

Disease Transmission ◽

Transmission Rate ◽

Disease Incidence ◽

Demographic Data ◽

School Closure ◽

Mitigation Measures ◽

Contact Tracing ◽

School Closures ◽

Infection Transmission ◽

The Impact

Among the various non–pharmaceutical interventions implemented in response to the Covid–19 pandemic during 2020, school closures have been in place in several countries to reduce infection transmission. Nonetheless, the significant short and long–term impacts of prolonged suspension of in–person classes is a major concern. There is still considerable debate around the best timing for school closure and reopening, its impact on the dynamics of disease transmission, and its effectiveness when considered in association with other mitigation measures. Despite the erratic implementation of mitigation measures in Brazil, school closures were among the first measures taken early in the pandemic in most of the 27 states in the country. Further, Brazil delayed the reopening of schools and stands among the countries in which schools remained closed for the most prolonged period in 2020. To assess the impact of school reopening and the effect of contact tracing strategies in rates of Covid–19 cases and deaths, we model the epidemiological dynamics of disease transmission in 3 large urban centers in Brazil under different epidemiological contexts. We implement an extended SEIR model stratified by age and considering contact networks in different settings – school, home, work, and elsewhere, in which the infection transmission rate is affected by various intervention measures. After fitting epidemiological and demographic data, we simulate scenarios with increasing school transmission due to school reopening. Our model shows that reopening schools results in a non–linear increase of reported Covid-19 cases and deaths, which is highly dependent on infection and disease incidence at the time of reopening. While low rates of within[&ndash]school transmission resulted in small effects on disease incidence (cases/100,000 pop), intermediate or high rates can severely impact disease trends resulting in escalating rates of new cases even if other interventions remain unchanged. When contact tracing and quarantining are restricted to school and home settings, a large number of daily tests is required to produce significant effects of reducing the total number of hospitalizations and deaths. Our results suggest that policymakers should carefully consider the epidemiological context and timing regarding the implementation of school closure and return of in-person school activities. Also, although contact tracing strategies are essential to prevent new infections and outbreaks within school environments, our data suggest that they are alone not sufficient to avoid significant impacts on community transmission in the context of school reopening in settings with high and sustained transmission rates.

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Estimating the impact of reopening schools on the reproduction number of SARS-CoV-2 in England, using weekly contact survey data

BMC Medicine ◽

10.1186/s12916-021-02107-0 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

James D. Munday ◽

Christopher I. Jarvis ◽

Amy Gimma ◽

Kerry L. M. Wong ◽

Kevin van Zandvoort ◽

...

Keyword(s):

Reproduction Number ◽

Transmission Rate ◽

Transmission Risk ◽

Individual Level ◽

Face To Face ◽

School Settings ◽

Contact Rates ◽

Contact Survey ◽

The Uk ◽

The Impact

Abstract Background Schools were closed in England on 4 January 2021 as part of increased national restrictions to curb transmission of SARS-CoV-2. The UK government reopened schools on 8 March. Although there was evidence of lower individual-level transmission risk amongst children compared to adults, the combined effects of this with increased contact rates in school settings and the resulting impact on the overall transmission rate in the population were not clear. Methods We measured social contacts of > 5000 participants weekly from March 2020, including periods when schools were both open and closed, amongst other restrictions. We combined these data with estimates of the susceptibility and infectiousness of children compared with adults to estimate the impact of reopening schools on the reproduction number. Results Our analysis indicates that reopening all schools under the same measures as previous periods that combined lockdown with face-to-face schooling would be likely to increase the reproduction number substantially. Assuming a baseline of 0.8, we estimated a likely increase to between 1.0 and 1.5 with the reopening of all schools or to between 0.9 and 1.2 reopening primary or secondary schools alone. Conclusion Our results suggest that reopening schools would likely halt the fall in cases observed between January and March 2021 and would risk a return to rising infections, but these estimates relied heavily on the latest estimates or reproduction number and the validity of the susceptibility and infectiousness profiles we used at the time of reopening.

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Evolution of disease transmission rate during the course of SARS-COV-2: Patterns and determinants

10.21203/rs.3.rs-44647/v1 ◽

2020 ◽

Author(s):

Jie Zhu ◽

Blanca Gallego

Keyword(s):

Public Health ◽

Disease Transmission ◽

Reproduction Number ◽

Transmission Rate ◽

Region Of Interest ◽

Health Interventions ◽

Transmission Model ◽

Public Health Interventions ◽

Containment Measures ◽

The Impact

Abstract To date, many studies have argued the potential impact of public health interventions on flattening the epidemic curve of SARS-CoV-2. Most of them have focused on simulating the impact of interventions in a region of interest by manipulating contact patterns and key transmission parameters to reflect different scenarios. Our study looks into the evolution of the daily effective reproduction number during the epidemic via a stochastic transmission model. We found this measure (although model-dependent) provides an early signal of the efficacy of containment measures. This epidemiological parameter when updated in real-time can also provide better predictions of future outbreaks. Our results found a substantial variation in the effect of public health interventions on the dynamic of SARS-CoV-2 transmission over time and across countries, that could not be explained solely by the timing and number of the adopted interventions. This suggests that further knowledge about the idiosyncrasy of their implementation and effectiveness is required. Although sustained containment measures have successfully lowered growth in disease transmission, more than half of the 101 studied countries failed to maintain the effective reproduction number close to or below 1. This resulted in continued growth in reported cases. Finally, we were able to predict with reasonable accuracy which countries would experience outbreaks in the next 30 days.

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Accessing the Syndemic of COVID-19 and Malaria Intervention in Africa

10.21203/rs.3.rs-52323/v1 ◽

2020 ◽

Author(s):

Benyun Shi ◽

Jinxin Zheng ◽

Shang Xia ◽

Shan Lin ◽

Xinyi Wang ◽

...

Keyword(s):

Malaria Transmission ◽

Sub Saharan Africa ◽

Cumulative Number ◽

Insecticide Treated Nets ◽

Middle Income ◽

Social Distancing ◽

Transmission Potential ◽

Epidemic Dynamics ◽

The Impact ◽

Epidemiological Parameters

Abstract Background: The COVID-19 pandemic has caused substantial disruptions to health services in the low and middle-income countries with a high burden of other diseases, such as malaria in sub-Saharan Africa. As the COVID-19 pandemic spread to Africa, there is an urgent need to assess the impact of COVID-19 pandemic on malaria transmission potential in malaria-endemic countries in Africa.Methods: We present a data-driven method to quantify the extent to which the COVID-19 pandemic, as well as various non-pharmaceutical interventions (NPIs), could lead to the change of malaria transmission potential in 2020. First, we adopt a particle Markov Chain Monte Carlo method to estimate epidemiological parameters in each country by fitting the time series of the cumulative number of COVID-19 cases. Then, we simulate the epidemic dynamics of COVID-19 under two groups of NPIs: (i) contact restriction and social distancing, and (ii) early identification and isolation of cases. Based on the simulated epidemic curves, we quantify the impact of COVID-19 epidemic and NPIs on the distribution of insecticide-treated nets (ITNs). Finally, by treating the total number of ITNs available in each country in 2020, we evaluate the negative effects of COVID-19 pandemic on malaria transmission potential based on the notion of vectorial capacity.Results: We conduct case studies in four malaria-endemic countries, Ethiopia, Nigeria, Tanzania, and Zambia, in Africa. The epidemiological parameters (i.e., the basic reproduction number R0 and the duration of infection DI ) of COVID-19 in each country are estimated as follows: Ethiopia (R0 = 1:57, DI = 5:32), Nigeria (R0 = 2:18, DI = 6:58), Tanzania (R0 = 2:47, DI = 6:01), and Zambia (R0 = 2:12, DI = 6:96). Based on the estimated epidemiological parameters, the epidemic curves simulated under various NPIs indicated that the earlier the interventions are implemented, the better the epidemic is controlled. Moreover, the effect of combined NPIs is better than contact restriction and social distancing only. By treating the total number of ITNs available in each country in 2020 as a baseline, our results show that even with stringent NPIs, malaria transmission potential will remain higher than expected in the second half of 2020.Conclusions: By quantifying the impact of various NPI response to the COVID-19 pandemic on malaria transmission potential, this study provides a way to jointly address the syndemic between COVID-19 and malaria in malaria-endemic countries in Africa. The results suggest that the early intervention of COVID-19 can effectively reduce the scale of the epidemic and mitigate its impact on malaria transmission potential.

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Interplay of social distancing and border restrictions for pandemics via the epidemic renormalisation group framework

Scientific Reports ◽

10.1038/s41598-020-72175-4 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Giacomo Cacciapaglia ◽

Francesco Sannino

Keyword(s):

Disease Transmission ◽

Large Scale ◽

Social Impact ◽

Renormalisation Group ◽

Group Approach ◽

Social Distancing ◽

Global Epidemic ◽

The World ◽

The Impact ◽

Large Scale Simulations

Abstract One of the biggest threats to humanity are pandemics. In our global society they can rage around the world with an immense toll in terms of human, economic and social impact. Forecasting the spreading of a pandemic is, therefore, paramount in helping governments to enforce a number of social and economic measures, apt at curbing the pandemic and dealing with its aftermath. We demonstrate that the epidemic renormalisation group approach to pandemics provides an effective and simple way to investigate the dynamics of disease transmission and spreading across different regions of the world. The framework also allows for reliable projections on the impact of travel limitations and social distancing measures on global epidemic spread. We test and calibrate it on reported COVID-19 cases while unveiling the mechanism that governs the delay in the relative peaks of newly infected cases among different regions of the globe. We discover that social distancing measures are more effective than travel limitations across borders in delaying the epidemic peak. We further provide the link to compartmental models such as the time-honoured SIR-like models. We also show how to generalise the framework to account for the interactions across several regions of the world, replacing or complementing large scale simulations.

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Mathematical Parameters of the COVID-19 Epidemic in Brazil and Evaluation of the Impact of Different Public Health Measures

Biology ◽

10.3390/biology9080220 ◽

2020 ◽

Vol 9 (8) ◽

pp. 220 ◽

Cited By ~ 3

Author(s):

Renato M. Cotta ◽

Carolina P. Naveira-Cotta ◽

Pierre Magal

Keyword(s):

Public Health ◽

Initial Conditions ◽

Transmission Rate ◽

Time Variable ◽

Health Interventions ◽

Public Health Interventions ◽

Problem Model ◽

Social Distancing ◽

Health Measures ◽

The Impact

A SIRU-type epidemic model is employed for the prediction of the COVID-19 epidemy evolution in Brazil, and analyze the influence of public health measures on simulating the control of this infectious disease. The proposed model allows for a time variable functional form of both the transmission rate and the fraction of asymptomatic infectious individuals that become reported symptomatic individuals, to reflect public health interventions, towards the epidemy control. An exponential analytical behavior for the accumulated reported cases evolution is assumed at the onset of the epidemy, for explicitly estimating initial conditions, while a Bayesian inference approach is adopted for the estimation of parameters by employing the direct problem model with the data from the first phase of the epidemy evolution, represented by the time series for the reported cases of infected individuals. The evolution of the COVID-19 epidemy in China is considered for validation purposes, by taking the first part of the dataset of accumulated reported infectious individuals to estimate the related parameters, and retaining the rest of the evolution data for direct comparison with the predicted results. Then, the available data on reported cases in Brazil from 15 February until 29 March, is used for estimating parameters and then predicting the first phase of the epidemy evolution from these initial conditions. The data for the reported cases in Brazil from 30 March until 23 April are reserved for validation of the model. Then, public health interventions are simulated, aimed at evaluating the effects on the disease spreading, by acting on both the transmission rate and the fraction of the total number of the symptomatic infectious individuals, considering time variable exponential behaviors for these two parameters. This first constructed model provides fairly accurate predictions up to day 65 below 5% relative deviation, when the data starts detaching from the theoretical curve. From the simulated public health intervention measures through five different scenarios, it was observed that a combination of careful control of the social distancing relaxation and improved sanitary habits, together with more intensive testing for isolation of symptomatic cases, is essential to achieve the overall control of the disease and avoid a second more strict social distancing intervention. Finally, the full dataset available by the completion of the present work is employed in redefining the model to yield updated epidemy evolution estimates.

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Impact of Social Distancing on COVID-19 and Other Related Infectious Disease Transmission: A Systematic Review

Open Access Macedonian Journal of Medical Sciences ◽

10.3889/oamjms.2021.7374 ◽

2021 ◽

Vol 9 (F) ◽

pp. 601-607

Author(s):

Nor Rumaizah Mohd Nordin ◽

Fadly Syah Arsad ◽

Puteri Sofia Nadira Megat Kamaruddin ◽

Muhammad Hilmi ◽

Mohd Faizal Madrim ◽

...

Keyword(s):

Infectious Disease ◽

Infectious Diseases ◽

South Korea ◽

Disease Transmission ◽

Research Question ◽

Control Measures ◽

Physical Contact ◽

Infectious Disease Transmission ◽

Social Distancing ◽

The Impact

Background Similar to other coronaviruses, COVID-19 is transmitted mainly by droplets and is highly transmissible through close proximity or physical contact with an infected person. Countries across the globe have implemented public health control measures to prevent onwards transmission and reduce burden on health care settings. Social or physical distancing was found to be one of appropriate measure based on previous experience with epidemic and pandemic contagious diseases. This study aims to review the latest evidence of the impact of social or physical distancing implemented during COVID-19 pandemic towards COVID-19 and other related infectious disease transmission. Methodology The study uses PRISMA review protocol and formulation of research question was based on PICO. The selected databases include Ovid MEDLINE and Scopus. Thorough identification, screening and eligibility process were done, revealed selected 8 articles. The articles then ranked in quality through MMAT. Results A total of eight papers included in this analysis. Five studies (USA, Canada, South Korea and the United Kingdom) showed physical distancing had resulted in a reduction in Covid-19 transmission. In comparison, three other studies (Australia, South Korea and Finland) showed a similar decline on other infectious diseases (Human Immunodeficiency Virus (HIV), other sexually transmitted infections (STI), Influenza, Respiratory Syncytial Virus (RSV) and Vaccine-Preventive Disease (VPD). The degree of the distancing policy implemented differ between strict and lenient, with both result in effectiveness in reducing transmission of infectious disease. Conclusion Physical or social distancing may come in the form of extreme or lenient measure in effectively containing contagious disease like COVID-19, however the stricter the measure will give more proportionate impact towards the economy, education, mental health issues, morbidity and mortality of non-COVID-19 diseases. Since we need this measure to ensure the reduction of infectious diseases transmission in order to help flattening the curve which allow much needed time for healthcare system to prepare adequately to response, ‘Precision physical distancing” can be implemented which will have more benefit towards the survival of the community as a whole.

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The interplay of policy, behavior, and socioeconomic conditions in early COVID-19 epidemiology in Georgia

10.1101/2021.03.24.21254256 ◽

2021 ◽

Author(s):

Mallory J Harris ◽

Ella Tessier-Lavigne ◽

Erin A Mordecai

Keyword(s):

Public Health ◽

Black Population ◽

Behavior Changes ◽

Protective Behavior ◽

Local Public Health ◽

Social Distancing ◽

Epidemic Dynamics ◽

Lower Mobility ◽

Reduced Mobility ◽

The Impact

To investigate the impact of local public health orders, behavior, and population factors on early epidemic dynamics, we investigated variation among counties in the U.S. state of Georgia. We conducted regressions to identify predictors of (1) local public health orders, (2) mobility as a proxy for behavior, and (3) epidemiological outcomes (i.e., cases and deaths). We used an event study to determine whether social distancing and shelter-in-place orders caused a change in mobility. Counties at greater risk for large early outbreaks (i.e., larger populations and earlier first cases) were more likely to introduce local public health orders. Social distancing orders gradually reduced mobility by 19% ten days after their introduction, and lower mobility was associated with fewer cases and deaths. Air pollution and population size were predictors of cases and deaths, while larger elderly or Black population were predictors of lower mobility and greater cases, suggesting self-protective behavior in vulnerable populations. Early epidemiological outcomes reflected responses to policy orders and existing health and socioeconomic disparities related to disease vulnerability and ability to socially distance. Teasing apart the impact of behavior changes and population factors is difficult because the epidemic is embedded in a complex social system with multiple potential feedbacks.

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