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

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.

scholarly journals Estimating Asymptomatic and Undetected Cases in the COVID-19 Outbreak in Wuhan

2020 ◽  
Author(s):  
Xi Huo ◽  
Jing Chen ◽  
Shigui Ruan
Keyword(s):  
Disease Transmission ◽  
Large Scale ◽  
Transmission Rate ◽  
Control Strategies ◽  
Herd Immunity ◽  
Antibody Prevalence ◽  
Screening Process ◽  
Nonpharmaceutical Interventions ◽  
Key Events ◽  
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 asymptomatic and undetected cases in the outbreak.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 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 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 adjusting reopen plans.

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

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.

scholarly journals Assessing the impact of low-technology emanators alongside long-lasting insecticidal nets to control malaria

2020 ◽  
Vol 376 (1818) ◽  
pp. 20190817 ◽  
Author(s):  
Joel Hellewell ◽  
Ellie Sherrard-Smith ◽  
Sheila Ogoma ◽  
Thomas S. Churcher
Keyword(s):  
Disease Transmission ◽  
Pyrethroid Resistance ◽  
Control Strategies ◽  
Indoor Residual Spraying ◽  
Additional Benefit ◽  
Sub Saharan Africa ◽  
Outdoor Biting ◽  
Mosquito Mortality ◽  
The Impact ◽  
Long Lasting Insecticidal Nets

Malaria control in sub-Saharan Africa relies on the widespread use of long-lasting insecticidal nets (LLINs) or the indoor residual spraying of insecticide. Disease transmission may be maintained even when these indoor interventions are universally used as some mosquitoes will bite in the early morning and evening when people are outside. As countries seek to eliminate malaria, they can target outdoor biting using new vector control tools such as spatial repellent emanators, which emit airborne insecticide to form a protective area around the user. Field data are used to incorporate a low-technology emanator into a mathematical model of malaria transmission to predict its public health impact across a range of scenarios. Targeting outdoor biting by repeatedly distributing emanators alongside LLINs increases the chance of elimination, but the additional benefit depends on the level of anthropophagy in the local mosquito population, emanator effectiveness and the pre-intervention proportion of mosquitoes biting outdoors. High proportions of pyrethroid-resistant mosquitoes diminish LLIN impact because of reduced mosquito mortality. When mosquitoes are highly anthropophagic, this reduced mortality leads to more outdoor biting and a reduced additional benefit of emanators, even if emanators are assumed to retain their effectiveness in the presence of pyrethroid resistance. Different target product profiles are examined, which show the extra epidemiological benefits of spatial repellents that induce mosquito mortality. This article is part of the theme issue ‘Novel control strategies for mosquito-borne diseases’.

RISK FACTORS AND PROTECTION FACTORS FOR THE COVID-19 EPIDEMIC AND VACCINATION AS A POSSIBLE MEANS OF CONTROLLING THE EPIDEMIC

2017 ◽  
Author(s):  
Oksana Romaniv ◽  
◽  
Bohdan Klyapchuk ◽  
Keyword(s):  
Risk Factors ◽  
Economic Integration ◽  
Herd Immunity ◽  
Vaccination Program ◽  
Special Method ◽  
Vaccination Programs ◽  
Regional Dynamics ◽  
Spatio Temporal ◽  
The Impact

A study of the impact of especially contextual on COVID-19 factors of the epidemic (geopolitical, climatic, socio-economic integration, social, including religious, demographic and others) was conducted. The regional dynamics of the epidemic in the Scandinavian countries was analyzed. The spatio-temporal changes of the epidemic indicators in the conditions of loyalty to risk factors (Sweden) and in the conditions of controlled risks (in other countries of the Scandinavian region) were revealed. The current research of scientists on the formation of herd immunity in the population with and without vaccination programs was generalized. The article evaluated the quality of the vaccination program in Ukraine. The threshold indicator "herd immunity" and the number of months to achieve herd immunity in Ukraine without vaccination were calculated according to a special method.

scholarly journals Systematic Review: The Impact of Socioeconomic Factors on Aedes aegypti Mosquito Distribution in the Mainland United States

2019 ◽  
Author(s):  
Whitney Holeva-Eklund ◽  
Timothy Behrens ◽  
Crystal Hepp
Keyword(s):  
United States ◽  
Aedes Aegypti ◽  
Socioeconomic Factors ◽  
Control Strategies ◽  
Anthropogenic Factors ◽  
Eligibility Criteria ◽  
Screening Process ◽  
Close Relationship ◽  
Meta Analyses ◽  
The Impact

Background: Aedes aegypti mosquitoes are primary vectors of dengue, yellow fever, chikungunya and Zika viruses. A. aegypti is highly anthropophilic and relies nearly exclusively on human blood meals and habitats for reproduction. Socioeconomic factors may influence the spread of A. aegypti due to their close relationship with humans. This paper describes and summarizes the published literature on how socioeconomic variables influence the distribution of A. aegypti mosquitoes in the mainland United States. Methods: A comprehensive search of PubMed/Medline, Scopus, Web of Science, and EBSCO Academic Search Complete through June 12, 2019 was used to retrieve all articles published in English on the association of socioeconomic factors and the distribution of A. aegypti mosquitoes. Articles were screened for eligibility using the process described in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: Initially, 3,493 articles were identified through the database searches and previously known literature. After checking for duplicates, 2,145 articles remained. These articles were screened for eligibility using their titles and abstracts, and 2,098 articles were excluded for not meeting the eligibility criteria. Finally, the full text for each of the remaining articles (n = 38) was read to determine eligibility. Through this screening process, 11 articles were identified for inclusion in this review. Conclusions: The findings for these 11 studies revealed inconsistent relationships between the studied socioeconomic factors and the distribution and abundance of A. aegypti. The findings of this review suggest a gap in the literature and understanding of the influence of anthropogenic factors on the distribution of A. aegypti that could hinder efforts to implement effective public health prevention and control strategies should a disease outbreak occur.

scholarly journals Clumpiness: Modeling the Impact of Social Dynamics on COVID-19 Spread

2021 ◽  
Author(s):  
Ben Goertzel ◽  
Cassio Pennachin ◽  
Deborah Duong ◽  
Matthew Iklé ◽  
Michael Duncan ◽  
...  
Keyword(s):  
Social Networks ◽  
Disease Transmission ◽  
Social Dynamics ◽  
Demographic Data ◽  
Herd Immunity ◽  
Control Policy ◽  
Policy Formation ◽  
Packet Transmission ◽  
Physical Proximity ◽  
The Impact

We present an agent based simulation supplemented with two novel social network interconnectivity measures, `clumpiness' and `hoprank,' that are the same concept defined at global and local levels, respectively. The measures may be computed from samples of readily available demographic data, and are useful for measuring probabilistic packet transmission through social networks. For simplicity, agents in our simulation group together through homophily, the principle of `like attracts like'. In three studies we apply clumpiness to measure the effects, on disease transmission, caused by social networks of both homophilic physical proximity and homophilic information replication. The particular characteristic we are interested in about disease transmission is herd immunity, the percentage of a population that has to be immune in order to prevent infection from spreading to those who are not. Two studies demonstrate innovations measuring herd immunity levels and predicting future outbreak locations, procedures relevant to epidemiological control policy. In the first study, we look at how homophilic physical proximity networks form natural bubbles that act as frictive surfaces that affect the speed of transmission of packets and influence herd immunity levels. In the second study, we test clumpiness in homophilic proximity social networks as a predictor of future infection outbreaks at the level of individual schools, restaurants, and workplaces. Our third study demonstrates that protective social bubbles form naturally from homophilic information replication networks, and enhance the natural bubbles that come from the homophilic physical proximity networks. Accurate description of this information environment lays the foundation for epidemiological messaging policy formation.

scholarly journals Modelling the Transmission Dynamics of COVID-19 in Six High-Burden Countries

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Azizur Rahman ◽  
Md Abdul Kuddus
Keyword(s):  
Older People ◽  
Disease Transmission ◽  
Transmission Rate ◽  
Control Strategies ◽  
Rank Correlation ◽  
Critical Parameters ◽  
High Burden ◽  
Susceptibility To Infection ◽  
The Usa ◽  
The Uk

The new Coronavirus Disease 19, officially known as COVID-19, originated in China in 2019 and has since spread worldwide. We presented an age-structured Susceptible-Latent-Mild-Critical-Removed (SLMCR) compartmental model of COVID-19 disease transmission with nonlinear incidence during the pandemic period. We provided the model calibration to estimate parameters with day-wise COVID-19 data, i.e., reported cases by worldometer from 15th February to 30th March 2020 in six high-burden countries, including Australia, Italy, Spain, the USA, the UK, and Canada. We estimate transmission rates for each country and found that the country with the highest transmission rate is Spain, which may increase the new cases and deaths than the other countries. We found that saturation infection negatively impacted the dynamics of COVID-19 cases in all the six high-burden countries. The study used a sensitivity analysis to identify the most critical parameters through the partial rank correlation coefficient method. We found that the transmission rate of COVID-19 had the most significant influence on prevalence. The prediction of new cases in COVID-19 until 30th April 2020 using the developed model was also provided with recommendations to control strategies of COVID-19. We also found that adults are more susceptible to infection than both children and older people in all six countries. However, in Italy, Spain, the UK, and Canada, older people show more susceptibility to infection than children, opposite to the case in Australia and the USA. The information generated from this study would be helpful to the decision-makers of various organisations across the world, including the Ministry of Health in Australia, Italy, Spain, the USA, the UK, and Canada, to control COVID-19.

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

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.

scholarly journals Modelling the transmission dynamics of COVID-19 in six high burden countries

2020 ◽  
Author(s):  
Azizur Rahman ◽  
Md Abdul Kuddus
Keyword(s):  
Disease Transmission ◽  
Compartmental Model ◽  
Transmission Rate ◽  
Control Strategies ◽  
Rank Correlation ◽  
Decision Makers ◽  
High Burden ◽  
Epidemic Period ◽  
Partial Rank Correlation ◽  
Coefficient Method

AbstractThe new coronavirus disease, officially known as COVID-19, originated in China in 2019 and has since spread around the globe. We presented a modified Susceptible-Latent-Infected-Removed (SLIR) compartmental model of COVID-19 disease transmission with nonlinear incidence during the epidemic period. We provided the model calibration to estimate parameters with day wise corona virus (COVID-19) data i.e. reported cases by worldometer from the period of 15th February to 30th March, 2020 in six high burden countries including Australia, Italy, Spain, USA, UK and Canada. We estimate transmission rates for each countries and found that the highest transmission rate country in Spain, which may be increase the new cases and deaths in Spain than the other countries. Sensitivity analysis was used to identify the most important parameters through the partial rank correlation coefficient method. We found that the transmission rate of COVID-19 had the largest influence on the prevalence. We also provides the prediction of new cases in COVID-19 until May 18, 2020 using the developed model and recommends, control strategies of COVID-19. The information that we generated from this study would be useful to the decision makers of various organizations across the world including the Ministry of Health in Australia, Italy, Spain, USA, UK and Canada to control COVID-19.

scholarly journals Evaluating the Effectiveness of Vaccines Using a Regression Discontinuity Design

2019 ◽  
Vol 188 (6) ◽  
pp. 987-990
Author(s):  
Nicole E Basta ◽  
M Elizabeth Halloran
Keyword(s):  
Randomized Clinical Trials ◽  
Regression Discontinuity ◽  
Control Strategies ◽  
Regression Discontinuity Design ◽  
Vaccination Programs ◽  
Wide Range ◽  
Quasi Experimental ◽  
Psychology Literature ◽  
And Control ◽  
The Impact

Abstract The regression discontinuity design (RDD), first proposed in the educational psychology literature and popularized in econometrics in the 1960s, has only recently been applied to epidemiologic research. A critical aim of infectious disease epidemiologists and global health researchers is to evaluate disease prevention and control strategies, including the impact of vaccines and vaccination programs. RDDs have very rarely been used in this context. This quasi-experimental approach using observational data is designed to quantify the effect of an intervention when eligibility for the intervention is based on a defined cutoff such as age or grade in school, making it ideally suited to estimating vaccine effects given that many vaccination programs and mass-vaccination campaigns define eligibility in this way. Here, we describe key features of RDDs in general, then specific scenarios, with examples, to illustrate that RDDs are an important tool for advancing our understanding of vaccine effects. We argue that epidemiologic researchers should consider RDDs when evaluating interventions designed to prevent and control diseases. This approach can address a wide range of research questions, especially those for which randomized clinical trials would present major challenges or be infeasible. Finally, we propose specific ways in which RDDs could advance future vaccine research.

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