Distancing Interventions Research Articles

Forecasting the Effectiveness of Interventions to Control the Second Wave of COVID-19 Outbreaks: A Modelling Study in 12 Countries of 6 Regions

Background: In most countries, it is hard to effectively control coronavirus disease 2019 (COVID-19). This study conducted the most comprehensive evaluation of the effects of pharmacological (like vaccination, pharmacotherapy ) and non-pharmacological (like isolation, social distancing and mask-wearing) interventions taken singly or in combination for the first time globally.Methods: We estimate that across these 12 countries that are different but presentative, interventions prevented or delayed roughly millions of confirmed cases. This study constructs mathematical model, which interventions includes vaccination, pharmacotherapy, isolation, social distancing and mask-wearing , and analyses the effect of these interventions used alone and in combination.Findings: The basic reproduction number (R0) of each country mostly range from 3 to 5. In terms of the effect of single intervention, for countries such as China, South Korea, Thailand, US, South Africa and Algeria, it is preferred to recommend these countries to adopt isolation to prevent and control the second wave of COVID-19 outbreak, while for countries such as Russia, UK, Saudi Arabia, India and Brazil, wearing masks is the best choice. Especially pharmacotherapy can play a good role in Iran. When combinations with different interventions were taken, the situation was different. For US, Brazil and Algeria, the combination of “Vaccination & Isolation & Wearing mask” is recommended in these countries to prevent and control the development of COVID-19, and the combination of “Isolation & Social distancing & Wearing mask” is recommended in UK and China. For the rest, we suggest that Russia, Iran, Saudi Arabia, India, Thailand and South Africa take the intervention measures of “Vaccination & Medical treatment & Isolation & Wearing mask”, “Vaccination & Medical treatment”, “Vaccination & Social distancing & Wearing mask”, “Medical treatment & Social distancing & Wearing mask”, “Vaccination & Medical Treatment & Isolation”, “Vaccination & Medical Treatment & Wearing mask”, respectively to deal with the second wave of outbreaks that may come by the end of this year.Interpretation: Our model is operable and selective for the prevention and control of epidemic situations in various countries. These findings may help policy makers in the 180+ countries where COVID-19 has been reported around the world to identify the most effective and socioeconomically acceptable measures to prevent and control the second wave of COVID-19 epidemic, and inform if when these policies should be deployed, intensified or replaced.Funding: This study was partly supported by the Bill & Melinda Gates Foundation (INV-005834), the Science and Technology Program of Fujian Province (No: 2020Y0002), the Xiamen New Coronavirus Prevention and Control Emergency Tackling Special Topic Program (No: 3502Z2020YJ03), and the Open Research Fund of State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics (SKLVD2019KF005).Declaration of Interests: The authors declare no competing interests.

Mathematical Model of COVID-19 Transmission Dynamics in South Korea: The Impacts of Travel Restrictions, Social Distancing, and Early Detection

The novel coronavirus disease (COVID-19) poses a severe threat to public health officials all around the world. The early COVID-19 outbreak in South Korea displayed significant spatial heterogeneity. The number of confirmed cases increased rapidly in the Daegu and Gyeongbuk (epicenter), whereas the spread was much slower in the rest of Korea. A two-patch mathematical model with a mobility matrix is developed to capture this significant spatial heterogeneity of COVID-19 outbreaks from 18 February to 24 March 2020. The mobility matrix is taken from the movement data provided by the Korea Transport Institute (KOTI). Some of the essential patch-specific parameters are estimated through cumulative confirmed cases, including the transmission rates and the basic reproduction numbers (local and global). Our simulations show that travel restrictions between the epicenter and the rest of Korea effectively prevented massive outbreaks in the rest of Korea. Furthermore, we explore the effectiveness of several additional strategies for the mitigation and suppression of Covid-19 spread in Korea, such as implementing social distancing and early diagnostic interventions.

Inferring True COVID19 Infection Rates From Deaths.

The novel coronavirus, SARS-CoV-2, commonly known as COVID19 has become a global pandemic in early 2020. The world has mounted a global social distancing intervention on a scale thought unimaginable prior to this outbreak; however, the economic impact and sustainability limits of this policy create significant challenges for government leaders around the world. Understanding the future spread and growth of COVID19 is further complicated by data quality issues due to high numbers of asymptomatic patients who may transmit the disease yet show no symptoms; lack of testing resources; failure of recovered patients to be counted; delays in reporting hospitalizations and deaths; and the co-morbidity of other life-threatening illnesses. We propose a Monte Carlo method for inferring true case counts from observed deaths using clinical estimates of Infection Fatality Ratios and Time to Death. Findings indicate that current COVID19 confirmed positive counts represent a small fraction of actual cases, and that even relatively effective surveillance regimes fail to identify all infectious individuals. We further demonstrate that the miscount also distorts officials' ability to discern the peak of an epidemic, confounding efforts to assess the efficacy of various interventions.

Public health interventions slowed but did not halt the spread of COVID‐19 in India

The government of India implemented social distancing interventions to contain the COVID‐19 epidemic. However, effects of these interventions on epidemic dynamics are yet to be understood. Rates of laboratory‐confirmed COVID‐19 infections per day and effective reproduction number (Rt ) were estimated for 7 periods (Pre‐lockdown, Lockdown Phases 1 to 4 and Unlock 1–2) according to nationally implemented interventions with phased relaxation. Adoption of these interventions was estimated using Google mobility data. Estimates at the national level and for 12 Indian states most affected by COVID‐19 are presented. Daily case rates ranged from 0.03 to 285.60/10 million people across 7 discrete periods in India. From 18 May to 31 July 2020, the NCT of Delhi had the highest case rate (999/10 million people/day), whereas Madhya Pradesh had the lowest (49/10 million/day). Average Rt was 1.99 (95% CI 1.93–2.06) and 1.39 (95% CI 1.38–1.40) for the entirety of India during the period from 22 March 2020 to 17 May 2020 and from 18 May 2020 to 31 July 2020, respectively. Median mobility in India decreased in all contact domains during the period from 22 March 2020 to 17 May 2020, with the lowest being 21% in retail/recreation, except home which increased to 129% compared to the 100% baseline value. Median mobility in the ‘Grocery and Pharmacy’ returned to levels observed before 22 March 2020 in Unlock 1 and 2, and the enhanced mobility in the Pharmacy sector needs to be investigated. The Indian government imposed strict contact mitigation, followed by a phased relaxation, which slowed the spread of COVID‐19 epidemic progression in India. The identified daily COVID‐19 case rates and Rt will aid national and state governments in formulating ongoing COVID‐19 containment plans. Furthermore, these findings may inform COVID‐19 public health policy in developing countries with similar settings to India.

Hospital Admission and Social Distancing Intervention in COVID-19 Pandemic: An Operations Management Perspective

COVID-19 is a highly contagious disease and has been spread to most countries of the world with unprecedented transmission speed. Medical resources and treatments provided by the healthcare system are most critical to reduce the mortality rate of COVID-19 and to contain the spread of COVID-19 by isolating infectious individuals. We introduce a modified SEIR model with consideration of individuals’ access to limited medical resources to characterize the vital role of critical medical resources during the pandemic. We discuss how the three different hospital admission policies (hierarchy, mixed, and Fangcang sheltered healthcare system) affect the spread of the disease and the number of deaths and infections. We find that Fangcang shelter system results in the least number of infections, deaths as well as occupied beds among all three systems. In addition, when the hospital capacity is relatively high or transmission rate of the mildly infected patient is not ignorable, a mixed system can result in a lower number of infections and deaths but higher occupied beds than a hierarchy system because it prevents the disease transmission to a great extent. Public policies of social distancing play an important role in moderating the spreads overtime. We investigate the performance of the three healthcare systems under a static social distancing policy. We find that, under a static social distancing policy, a Fangcang shelter system results in the largest reduction in infections and deaths. Moreover, we conduct simulations on two types of dynamic lockdown policies and find that an effective dynamic lockdown policy should smooth the burden of a healthcare system and keeping minimum disruption of economic activities. That is, a lockdown that occurs either too early or two late could result in more deaths and infections.

A clash of epidemics: Impact of the COVID-19 pandemic response on opioid overdose

Coronavirus disease 2019 (COVID-19) will have a lasting impact on public health. In addition to the direct effects of COVID-19 infection, physical distancing and quarantine interventions have indirect effects on health. While necessary, physical distancing interventions to control the spread of COVID-19 could have multiple impacts on people living with opioid use disorder, including impacts on mental health that lead to greater substance use, the availability of drug supply, the ways that people use drugs, treatment-seeking behaviors, and retention in care. The degree to which COVID-19 will impact the opioid epidemic and through which of the possible mechanisms that we discuss is important to monitor. We employed simulation modeling to demonstrate the potential impact of physical distancing on overdose mortality.

COVID-19 and the "Film Your Hospital" Conspiracy Theory: Social Network Analysis of Twitter Data.

BackgroundDuring the COVID-19 pandemic, a number of conspiracy theories have emerged. A popular theory posits that the pandemic is a hoax and suggests that certain hospitals are “empty.” Research has shown that accepting conspiracy theories increases the likelihood that an individual may ignore government advice about social distancing and other public health interventions. Due to the possibility of a second wave and future pandemics, it is important to gain an understanding of the drivers of misinformation and strategies to mitigate it.ObjectiveThis study set out to evaluate the #FilmYourHospital conspiracy theory on Twitter, attempting to understand the drivers behind it. More specifically, the objectives were to determine which online sources of information were used as evidence to support the theory, the ratio of automated to organic accounts in the network, and what lessons can be learned to mitigate the spread of such a conspiracy theory in the future.MethodsTwitter data related to the #FilmYourHospital hashtag were retrieved and analyzed using social network analysis across a 7-day period from April 13-20, 2020. The data set consisted of 22,785 tweets and 11,333 Twitter users. The Botometer tool was used to identify accounts with a higher probability of being bots.ResultsThe most important drivers of the conspiracy theory are ordinary citizens; one of the most influential accounts is a Brexit supporter. We found that YouTube was the information source most linked to by users. The most retweeted post belonged to a verified Twitter user, indicating that the user may have had more influence on the platform. There was a small number of automated accounts (bots) and deleted accounts within the network.ConclusionsHashtags using and sharing conspiracy theories can be targeted in an effort to delegitimize content containing misinformation. Social media organizations need to bolster their efforts to label or remove content that contains misinformation. Public health authorities could enlist the assistance of influencers in spreading antinarrative content.

Low-impact social distancing interventions to mitigate local epidemics of SARS-CoV-2

Many jurisdictions implemented intensive social distancing to suppress SARS-CoV-2 transmission. The challenge now is to mitigate the ongoing COVID-19 epidemic without overburdening economic and social activities. An agent-based model simulated the population of King County, Washington. SARS-CoV-2 transmission probabilities were estimated by fitting simulated to observed hospital admissions. Interventions considered included encouraging telecommuting, reducing contacts to high-risk persons, and reductions to contacts outside of the home, among others. Removing all existing interventions would result in nearly 42,000 COVID-19 hospitalizations between June 2020 and January 2021, with peak hospital occupancy exceeding available beds 6-fold. Combining interventions is predicted to reduce total hospitalizations by 48% (95% CI, 47–49%), with peak COVID-19 hospital occupancy of 70% of total beds. Targeted school closures can further reduce the peak occupancy. Combining low-impact interventions may mitigate the course of the COVID-19 epidemic, keeping hospital burden within the capacity of the healthcare system.

Flexible, Freely Available Stochastic Individual Contact Model for Exploring COVID-19 Intervention and Control Strategies: Development and Simulation.

BackgroundThroughout March 2020, leaders in countries across the world were making crucial decisions about how and when to implement public health interventions to combat the coronavirus disease (COVID-19). They urgently needed tools to help them to explore what will work best in their specific circumstances of epidemic size and spread, and feasible intervention scenarios.ObjectiveWe sought to rapidly develop a flexible, freely available simulation model for use by modelers and researchers to allow investigation of how various public health interventions implemented at various time points might change the shape of the COVID-19 epidemic curve.Methods“COVOID” (COVID-19 Open-Source Infection Dynamics) is a stochastic individual contact model (ICM), which extends the ICMs provided by the open-source EpiModel package for the R statistical computing environment. To demonstrate its use and inform urgent decisions on March 30, 2020, we modeled similar intervention scenarios to those reported by other investigators using various model types, as well as novel scenarios. The scenarios involved isolation of cases, moderate social distancing, and stricter population “lockdowns” enacted over varying time periods in a hypothetical population of 100,000 people. On April 30, 2020, we simulated the epidemic curve for the three contiguous local areas (population 287,344) in eastern Sydney, Australia that recorded 5.3% of Australian cases of COVID-19 through to April 30, 2020, under five different intervention scenarios and compared the modeled predictions with the observed epidemic curve for these areas.ResultsCOVOID allocates each member of a population to one of seven compartments. The number of times individuals in the various compartments interact with each other and their probability of transmitting infection at each interaction can be varied to simulate the effects of interventions. Using COVOID on March 30, 2020, we were able to replicate the epidemic response patterns to specific social distancing intervention scenarios reported by others. The simulated curve for three local areas of Sydney from March 1 to April 30, 2020, was similar to the observed epidemic curve in terms of peak numbers of cases, total numbers of cases, and duration under a scenario representing the public health measures that were actually enacted, including case isolation and ramp-up of testing and social distancing measures.ConclusionsCOVOID allows rapid modeling of many potential intervention scenarios, can be tailored to diverse settings, and requires only standard computing infrastructure. It replicates the epidemic curves produced by other models that require highly detailed population-level data, and its predicted epidemic curve, using parameters simulating the public health measures that were enacted, was similar in form to that actually observed in Sydney, Australia. Our team and collaborators are currently developing an extended open-source COVOID package comprising of a suite of tools to explore intervention scenarios using several categories of models.

Anticipating the Novel Coronavirus Disease (COVID-19) Pandemic.

The COVID-19 outbreak was first declared an international public health, and it was later deemed a pandemic. In most countries, the COVID-19 incidence curve rises sharply over a short period of time, suggesting a transition from a disease-free (or low-burden disease) equilibrium state to a sustained infected (or high-burden disease) state. Such a transition is often known to exhibit characteristics of “critical slowing down.” Critical slowing down can be, in general, successfully detected using many statistical measures, such as variance, lag-1 autocorrelation, density ratio, and skewness. Here, we report an empirical test of this phenomena on the COVID-19 datasets of nine countries, including India, China, and the United States. For most of the datasets, increases in variance and autocorrelation predict the onset of a critical transition. Our analysis suggests two key features in predicting the COVID-19 incidence curve for a specific country: (a) the timing of strict social distancing and/or lockdown interventions implemented and (b) the fraction of a nation's population being affected by COVID-19 at that time. Furthermore, using satellite data of nitrogen dioxide as an indicator of lockdown efficacy, we found that countries where lockdown was implemented early and firmly have been successful in reducing COVID-19 spread. These results are essential for designing effective strategies to control the spread/resurgence of infectious pandemics.

Patterns of the COVID-19 pandemic spread around the world: exponential versus power laws.

We have analysed the COVID-19 epidemic data of more than 174 countries (excluding China) in the period between 22 January and 28 March 2020. We found that some countries (such as the USA, the UK and Canada) follow an exponential epidemic growth, while others (like Italy and several other European countries) show a power law like growth. Regardless of the best fitting law, many countries can be shown to follow a common trajectory that is similar to Italy (the epicentre at the time of analysis), but with varying degrees of delay. We found that countries with ‘younger’ epidemics, i.e. countries where the epidemic started more recently, tend to exhibit more exponential like behaviour, while countries that were closer behind Italy tend to follow a power law growth. We hypothesize that there is a universal growth pattern of this infection that starts off as exponential and subsequently becomes more power law like. Although it cannot be excluded that this growth pattern is a consequence of social distancing measures, an alternative explanation is that it is an intrinsic epidemic growth law, dictated by a spatially distributed community structure, where the growth in individual highly mixed communities is exponential but the longer term, local geographical spread (in the absence of global mixing) results in a power law. This is supported by computer simulations of a metapopulation model that gives rise to predictions about the growth dynamics that are consistent with correlations found in the epidemiological data. Therefore, seeing a deviation from straight exponential growth may be a natural progression of the epidemic in each country. On the practical side, this indicates that (i) even in the absence of strict social distancing interventions, exponential growth is not an accurate predictor of longer term infection spread, and (ii) a deviation from exponential spread and a reduction of estimated doubling times do not necessarily indicate successful interventions, which are instead indicated by a transition to a reduced power or by a deviation from power law behaviour.

How Effective Is Social Distancing?

We identify the dynamic effects of social distancing policy on reducing the transmission of the COVID-19 spread. We build a model that measures the relative frequency and geographic distribution of the virus growth rate and provides hypothetical infection distribution in the states that enacted the social distancing policy, where we control all time-varying, observed and unobserved, state-level heterogeneities. We apply our model to a panel of weekly COVID-19 infection cases and deaths of all states in the United States from February 20 to April 20, 2020, and find that during our sample period, social distancing intervention is effective in reducing the weekly growth rate in cases by 9.8% and in deaths by 7.0%. We show that the effects are time-varying that range from the weakest at the beginning of policy intervention to the strongest by the end of our sample period. We further demonstrate that the effects are cross-sectional heterogeneous as the states with higher income, higher education, more White people, more democratic voters, and higher CNN viewership have a more considerable reduction in the infection growth rate.

Abrupt Subsidence of Seasonal Influenza after COVID-19 Outbreak, Hong Kong, China.

The onset of the 2019–20 winter influenza season in Hong Kong coincided with the emergence of the coronavirus disease epidemic in neighboring mainland China. After widespread adoption of large-scale social distancing interventions in response to the impending coronavirus disease outbreak, the influenza season ended abruptly with a decrease to a low trough.

Epidemiological Modeling of COVID-19 in Saudi Arabia: Spread Projection, Awareness, and Impact of Treatment

The first case of COVID-19 originated in Wuhan, China, after which it spread across more than 200 countries. By 21 July 2020, the rapid global spread of this disease had led to more than 15 million cases of infection, with a mortality rate of more than 4.0% of the total number of confirmed cases. This study aimed to predict the prevalence of COVID-19 and to investigate the effect of awareness and the impact of treatment in Saudi Arabia. In this paper, COVID-19 data were sourced from the Saudi Ministry of Health, covering the period from 31 March 2020 to 21 July 2020. The spread of COVID-19 was predicted using four different epidemiological models, namely the susceptible–infectious–recovered (SIR), generalized logistic, Richards, and Gompertz models. The assessment of models’ fit was performed and compared using four statistical indices (root-mean-square error (RMSE), R squared (R2), adjusted R2 ( Radj2), and Akaike’s information criterion (AIC)) in order to select the most appropriate model. Modified versions of the SIR model were utilized to assess the influence of awareness and treatment on the prevalence of COVID-19. Based on the statistical indices, the SIR model showed a good fit to reported data compared with the other models (RMSE = 2790.69, R2 = 99.88%, Radj2 = 99.98%, and AIC = 1796.05). The SIR model predicted that the cumulative number of infected cases would reach 359,794 and that the pandemic would end by early September 2020. Additionally, the modified version of the SIR model with social distancing revealed that there would be a reduction in the final cumulative epidemic size by 9.1% and 168.2% if social distancing were applied over the short and long term, respectively. Furthermore, different treatment scenarios were simulated, starting on 8 July 2020, using another modified version of the SIR model. Epidemiological modeling can help to predict the cumulative number of cases of infection and to understand the impact of social distancing and pharmaceutical intervention on the prevalence of COVID-19. The findings from this study can provide valuable information for governmental policymakers trying to control the spread of this pandemic.

Act early, save lives: managing COVID-19 in Greece

ObjectivesThe aim of the study was to assess the impact of social distancing interventions in Greece and to examine what would have happened if those interventions had not been implemented. Study designA dynamic, discrete time, stochastic individual-based model was developed to simulate coronavirus disease 2019 (COVID-19) transmission. MethodsThe model was fitted to the observed trends in COVID-19 deaths and intensive care unit (ICU) bed use in Greece. ResultsIf Greece had not implemented social distancing interventions, the healthcare system would have been overwhelmed between March 30 and April 4. The combined social distancing interventions and increase in ICU beds averted 4360 (95% credible interval: 3050, 5700) deaths and prevented the healthcare system from becoming overwhelmed. ConclusionsThe quick and accurate interventions of the Greek government limited the burden of the COVID-19 outbreak.

Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK

Background: Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which have resulted in reduced mobility across different regions. Crowd level data on mobile phone usage can be used as a proxy for actual population mobility patterns and provide a way of quantifying the impact of social distancing measures on changes in mobility. Methods: Here, we use two mobile phone-based datasets (anonymised and aggregated crowd level data from O2 and from the Facebook app on mobile phones) to assess changes in average mobility, both overall and broken down into high and low population density areas, and changes in the distribution of journey lengths. Results: We show that there was a substantial overall reduction in mobility, with the most rapid decline on the 24th March 2020, the day after the Prime Minister’s announcement of an enforced lockdown. The reduction in mobility was highly synchronized across the UK. Although mobility has remained low since 26th March 2020, we detect a gradual increase since that time. We also show that the two different datasets produce similar trends, albeit with some location-specific differences. We see slightly larger reductions in average mobility in high-density areas than in low-density areas, with greater variation in mobility in the high-density areas: some high-density areas eliminated almost all mobility. Conclusions: These analyses form a baseline from which to observe changes in behaviour in the UK as social distancing is eased and inform policy towards the future control of SARS-CoV-2 in the UK.

Willingness to pay for morbidity and mortality risk reductions during an epidemic. Theory and preliminary evidence from COVID-19.

The COVID-19 pandemic and the strong social distancing measures adopted by governments around the world provide an ideal scenario to evaluate the trade-off between lives saved and morbidity avoided on the one hand and reduced economic resources on the other. We adapt the standard model of willingness to pay (WTP) for mortality/morbidity risk reductions by incorporating a number of aspects that are highly relevant during an epidemic; namely, health-care capacity constraints, dynamic aspects of prevention (i.e., interventions aimed at flattening the epidemic curve), and distributional issues due to high heterogeneity in the underlying risks. The calibration of the model generates a WTP of the order of 24% of GDP. We conclude that the benefits in terms of lives saved and morbidity avoided can well justify the enormous economic costs generated by social distancing interventions. There is, however, significant that heterogeneity in WTP estimates depending on the degree of vulnerability to infection risk (e.g., by age), implying a large redistribution of income and well-being.

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

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.

Estimation of Coronavirus Disease Case-Fatality Risk in Real Time

Abstract By April 2, 2020, >1 million persons worldwide were infected with severe acute respiratory syndrome coronavirus 2. We used a mathematical model to investigate the effectiveness of social distancing interventions in a mid-sized city. Interventions reduced contacts of adults >60 years of age, adults 20–59 years of age, and children <19 years of age for 6 weeks. Our results suggest interventions started earlier in the epidemic delay the epidemic curve and interventions started later flatten the epidemic curve. We noted that, while social distancing interventions were in place, most new cases, hospitalizations, and deaths were averted, even with modest reductions in contact among adults. However, when interventions ended, the epidemic rebounded. Our models suggest that social distancing can provide crucial time to increase healthcare capacity but must occur in conjunction with testing and contact tracing of all suspected cases to mitigate virus transmission.

Multiple Epidemic Wave Model of the COVID-19 Pandemic: Modeling Study.

BackgroundIntervention measures have been implemented around the world to mitigate the spread of the coronavirus disease (COVID-19) pandemic. Understanding the dynamics of the disease spread and the effectiveness of the interventions is essential in predicting its future evolution.ObjectiveThe aim of this study is to simulate the effect of different social distancing interventions and investigate whether their timing and stringency can lead to multiple waves (subepidemics), which can provide a better fit to the wavy behavior observed in the infected population curve in the majority of countries.MethodsWe have designed and run agent-based simulations and a multiple wave model to fit the infected population data for many countries. We have also developed a novel Pandemic Response Index to provide a quantitative and objective way of ranking countries according to their COVID-19 response performance.ResultsWe have analyzed data from 18 countries based on the multiple wave (subepidemics) hypothesis and present the relevant parameters. Multiple waves have been identified and were found to describe the data better. The effectiveness of intervention measures can be inferred by the peak intensities of the waves. Countries imposing fast and stringent interventions exhibit multiple waves with declining peak intensities. This result strongly corroborated with agent-based simulations outcomes. We also provided an estimate of how much lower the number of infections could have been if early and strict intervention measures had been taken to stop the spread at the first wave, as actually happened for a handful of countries. A novel index, the Pandemic Response Index, was constructed, and based on the model’s results, an index value was assigned to each country, quantifying in an objective manner the country’s response to the pandemic.ConclusionsOur results support the hypothesis that the COVID-19 pandemic can be successfully modeled as a series of epidemic waves (subepidemics) and that it is possible to infer to what extent the imposition of early intervention measures can slow the spread of the disease.