Comparative Analysis of COVID-19 Transmission Patterns in Three Chinese Regions vs. South Korea, Italy and Iran

AbstractBackgroundThe outbreak of Coronavirus 2019 (COVID-19) began in January 2020 in the city of Wuhan (Hubei province, China). It took about 2 months for China to get this infectious disease under control in its epicenter at Wuhan. Since February 2020, COVID-19 has been spreading around the world, becoming widespread in a number of countries. The timing and nature of government actions in response to the pandemic has varied from country to country, and their role in affecting the spread of the disease has been debated.MethodThe present study proposed a modified susceptible-exposed-infected-removed model (SEIR) model to perform a comparative analysis of the temporal progress of disease spread in six regions worldwide: three Chinese regions (Zhejiang, Guangdong and Xinjiang) vs. three countries (South Korea, Italy and Iran). For each region we developed detailed timelines of reported infections and outcomes, along with government- implemented measures to enforce social distancing. Simulations of the imposition of strong social distancing measures were used to evaluate the impact that these measures might have had on the duration and severity of COVID-19 outbreaks in the three countries.ResultsThe main results of this study are as follows: (a) an empirical COVID-19 growth law provides an excellent fit to the disease data in all study regions and potentially could be of more general validity; (b) significant differences exist in the spread characteristics of the disease among the three regions of China and between the three regions of China and the three countries; (c) under the control measures implemented in the Chinese regions (including the immediate quarantine of infected patients and their close contacts, and considerable restrictions on social contacts), the transmission rate of COVID-19 followed a modified normal distribution function, and it reached its peak after 1 to 2 days and then was reduced to zero 11, 11 and 18 days after a 1st-Level Response to Major Public Health Emergency was declared in Zhejiang, Guangdong and Xinjiang, respectively; moreover, the epidemic control times in Zhejiang, Guangdong and Xinjiang showed that the epidemic reached an “inflection point” after 9, 12 and 17 days, respectively, after a 1st-Level Response was issued; (d) an empirical COVID-19 law provided an excellent fit to the disease data in the six study regions, and the law can be potentially of more general validity; and (e) the curves of infected cases in South Korea, Italy and Iran would had been significantly flattened and shrunken at a relatively earlier stage of the epidemic if similar preventive measures as in the Chinese regions had been also taken in the above three countries on February 25th, February 25th and March 8th, respectively: the simulated maximum number of infected individuals in South Korea, Italy and Iran would had been 4480 cases (March 9th, 2020), 2335 cases (March 10th) and 6969 cases (March 20th), instead of the actual (reported) numbers of 7212 cases (March 9th), 8514 cases (March 10th, 2020) and 11466 cases (March 20th), respectively; moreover, up to March 29th, the simulated reduction in the accumulated number of infected cases would be 1585 for South Korea, 93490 for Italy and 23213 for Iran, respectively, accounting for 16.41% (South Korea), 95.70% (Italy) and 60.59% (Iran) of the accumulated number of actual reported infected cases.ConclusionsThe implemented measures in China were very effective for controlling the spread of COVID-19. These measures should be taken as early as possible, including the early identification of all infection sources and eliminating transmission pathways. Subsequently, the number of infected cases can be controlled at a low level, and existing medical resources could be sufficient for maintaining higher cure rates and lower mortality rate compared to the current situations in these countries. The proposed model can account for these prevention and control measures by properly adjusting its parameters, it computes the corresponding variations in disease transmission rate during the outbreak period, and it can provide valuable information for public health decision- making purposes.

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Control Strategies for the Third wave of COVID-19 infection in India: A Mathematical Model Incorporating Vaccine Effectiveness

10.36227/techrxiv.17705957 ◽

2022 ◽

Author(s):

Ashutosh Mahajan ◽

Namitha Sivadas ◽

Pooja Panda

Keyword(s):

Transmission Rate ◽

Control Strategies ◽

Control Measures ◽

Disease Spread ◽

Effective Control ◽

Third Wave ◽

Social Distancing ◽

Vaccination Rates ◽

New Variant ◽

The Future

The waning effectiveness of the COVID-19 vaccines and the emergence of a new variant Omicron has given rise to the possibility of another outbreak of the infection in India. COVID-19 has caused more than 34 million reported cases and 475 thousand deaths in India so far, and it has affected the country at the root level, socially as well as economically. After going through different control measures, mass vaccination has been achieved to a large extent for the highly populous country, and currently under progress. India has already been hit by a massive second wave of infection in April-June, 2021 mainly due to the delta variant, and might see a third wave in the near future that needs to be controlled with effective control strategies. In this paper, we present a compartmental epidemiological model with vaccinations incorporating the dose-dependent effectiveness. We study a possible sudden outbreak of SARS-CoV2 variants in the future, and bring out the associated predictions for various vaccination rates and point out optimum control measures. Our results show that for transmission rate 30% higher than the current rate due to emergence of new variant or relaxation of social distancing conditions, daily new cases can peak to 250k in March 2022, taking the second dose effectiveness dropping to 50% in the future. A combination of vaccination and controlled lockdown or social distancing is the key to tackling the current situation and for the coming few months. Our simulation results show that social distancing measures show better control over the disease spread than the higher vaccination rates. <br>

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Individually Optimal Choices can be Collectively Disastrous in COVID-19 Disease Control

10.21203/rs.3.rs-143573/v1 ◽

2021 ◽

Author(s):

Madison Stoddard ◽

Debra Van Egeren ◽

Kaitlyn Johnson ◽

Smriti Rao ◽

Josh Furgeson ◽

...

Keyword(s):

Public Health ◽

Disease Control ◽

Control Measures ◽

Disease Spread ◽

Complete Suppression ◽

Natural Immunity ◽

Endemic Disease ◽

Health Measures ◽

The Impact

Abstract Background: The word ‘pandemic’ conjures dystopian images of bodies stacked in the streets and societies on the brink of collapse. Despite this frightening picture, denialism and noncompliance with public health measures are common in the historical record, for example during the 1918 Influenza pandemic or the 2015 Ebola epidemic. The unique characteristics of SARS-CoV-2—its high basic reproduction number (R0), time-limited natural immunity and considerable potential for asymptomatic spread—exacerbate the public health repercussions of noncompliance with interventions (such as vaccines and masks) to limit disease transmission. Our work explores the rationality and impact of noncompliance with COVID-19 disease control measures. Methods: In this work, we used game theory to explore when noncompliance confers a perceived benefit to individuals. We then used epidemiological modeling to predict the impact of noncompliance on control of COVID-19, demonstrating that the presence of a noncompliant subpopulation prevents suppression of disease spread. Results: Our modeling demonstrating that noncompliance is a Nash equilibrium under a broad set of conditions, and that the existence of a noncompliant population can result in extensive endemic disease in the long-term after a return to pre-pandemic social and economic activity. Endemic disease poses a threat for both compliant and noncompliant individuals; all community members are protected if complete suppression is achieved, which is only possible with a high degree of compliance. For interventions that are highly effective at preventing disease spread, however, the consequences of noncompliance are borne disproportionately by noncompliant individuals. Conclusions: In sum, our work demonstrates the limits of free-market approaches to compliance with disease control measures during a pandemic. The act of noncompliance with disease intervention measures creates a negative externality, rendering COVID-19 disease control ineffective in the short term and making complete suppression impossible in the long term. Our work underscores the importance of developing effective strategies for prophylaxis through public health measures aimed at complete suppression and the need to focus on compliance at a population level.

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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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Control Strategies for the Third wave of COVID-19 infection in India: A Mathematical Model Incorporating Vaccine Effectiveness

10.1101/2021.12.27.21268464 ◽

2021 ◽

Author(s):

Namitha A Sivadas ◽

Ashutosh Mahajan ◽

Pooja Panda

Keyword(s):

Transmission Rate ◽

Control Strategies ◽

Control Measures ◽

Disease Spread ◽

Effective Control ◽

Third Wave ◽

Social Distancing ◽

Vaccination Rates ◽

New Variant ◽

The Future

The waning effectiveness of the COVID-19 vaccines and the emergence of a new variant Omicron has given rise to the possibility of another outbreak of the infection in India. COVID-19 has caused more than 34 million reported cases and 475 thousand deaths in India so far, and it has affected the country at the root level, socially as well as economically. After going through different control measures, mass vaccination has been achieved to a large extent for the highly populous country, and currently under progress. India has already been hit by a massive second wave of infection in April-June, 2021 mainly due to the delta variant, and might see a third wave in the near future that needs to be controlled with effective control strategies. In this paper, we present a compartmental epidemiological model with vaccinations incorporating the dose-dependent effectiveness. We study a possible sudden outbreak of SARS-CoV2 variants in the future, and bring out the associated predictions for various vaccination rates and point out optimum control measures. Our results show that for transmission rate 30% higher than the current rate due to emergence of new variant or relaxation of social distancing conditions, daily new cases can peak to 250k in March 2022, taking the second dose effectiveness dropping to 50% in the future. Combination of vaccination and controlled lockdown or social distancing is the key to tackling the current situation and for the coming few months. Our simulation results show that social distancing measures show better control over the disease spread than the higher vaccination rates.

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The effect of control measures on COVID-19 transmission in South Korea

PLoS ONE ◽

10.1371/journal.pone.0249262 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0249262

Author(s):

Taeyong Lee ◽

Hee-Dae Kwon ◽

Jeehyun Lee

Keyword(s):

Transmission Rate ◽

School Closure ◽

Control Measures ◽

After School ◽

Epidemic Threshold ◽

Social Distancing ◽

Control And Prevention ◽

Age Structured ◽

Net Reproduction ◽

The Impact

Countries around the world have taken control measures to mitigate the spread of COVID-19, including Korea. Social distancing is considered an essential strategy to reduce transmission in the absence of vaccination or treatment. While interventions have been successful in controlling COVID-19 in Korea, maintaining the current restrictions incurs great social costs. Thus, it is important to analyze the impact of different polices on the spread of the epidemic. To model the COVID-19 outbreak, we use an extended age-structured SEIR model with quarantine and isolation compartments. The model is calibrated to age-specific cumulative confirmed cases provided by the Korea Disease Control and Prevention Agency (KDCA). Four control measures—school closure, social distancing, quarantine, and isolation—are investigated. Because the infectiousness of the exposed has been controversial, we study two major scenarios, considering contributions to infection of the exposed, the quarantined, and the isolated. Assuming the transmission rate would increase more than 1.7 times after the end of social distancing, a second outbreak is expected in the first scenario. The epidemic threshold for increase of contacts between teenagers after school reopening is 3.3 times, which brings the net reproduction number to 1. The threshold values are higher in the second scenario. If the average time taken until isolation and quarantine reduces from three days to two, cumulative cases are reduced by 60% and 47% in the first scenario, respectively. Meanwhile, the reduction is 33% and 41%, respectively, for rapid isolation and quarantine in the second scenario. Without social distancing, a second wave is possible, irrespective of whether we assume risk of infection by the exposed. In the non-infectivity of the exposed scenario, early detection and isolation are significantly more effective than quarantine. Furthermore, quarantining the exposed is as important as isolating the infectious when we assume that the exposed also contribute to infection.

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Ohio Response to COVID-19 and Its Impact on Interventional Pain Management Practices

January 2018 - Pain Physician ◽

10.36076/ppj.2020/23/s439 ◽

2020 ◽

Vol 4S;23 (8;4S) ◽

pp. S439-S447

Author(s):

Amol Soin

Keyword(s):

Pain Management ◽

Infection Control ◽

Management Practices ◽

Substantial Reduction ◽

Control Measures ◽

Disease Spread ◽

Social Distancing ◽

Infection Control Measures ◽

Interventional Pain Management ◽

The Impact

Background: The coronavirus disease 2019 (COVID-19) pandemic has drastically altered daily living and medical care for Ohio residents and the practice of medicine for the interventional pain management physician. As a state, Ohio tends to be demographically representative of the broader US population. Objective: Reviewing the efforts deployed by Ohio to flatten the COVID-19 infection curve and reduce the spread of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an important component of determining optimal procedures for mitigating the effects of the COVID-19 pandemic. Methods: Over the course of several announcements and orders during the months of March and April, new policies were put into place to prevent COVID-19 transmission, which included efforts to facilitate social distancing and ensure the health care system could manage the number of COVID-19 cases at peak infection rate. Efforts directed toward medical providers included delay of elective procedures, expansion of telehealth options, and new temporary guidance for prescribing controlled substances. Results: The Ohio COVID-19 containment approach resulted in a substantial reduction in COVID-19 cases compared with early models of disease spread, and the state has begun a phased reopening. Continued vigilance in applying social distancing and infection control measures will be a critical component of preventing or reducing the impact of a second wave of COVID-19 in Ohio. Limitations: A narrative review with paucity of literature. Key words: COVID-19, infection rates, mitigating effects, pandemic, infection control

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Control Strategies for the Third wave of COVID-19 infection in India: A Mathematical Model Incorporating Vaccine Effectiveness

10.36227/techrxiv.17705957.v1 ◽

2022 ◽

Author(s):

Ashutosh Mahajan ◽

Namitha Sivadas ◽

Pooja Panda

Keyword(s):

Transmission Rate ◽

Control Strategies ◽

Control Measures ◽

Disease Spread ◽

Effective Control ◽

Third Wave ◽

Social Distancing ◽

Vaccination Rates ◽

New Variant ◽

The Future

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Impact of public health interventions to curb SARS-CoV-2 spread assessed by an evidence-educated Delphi panel and tailored SEIR model

Journal of Public Health ◽

10.1007/s10389-021-01566-2 ◽

2021 ◽

Author(s):

Bernd Brüggenjürgen ◽

Hans-Peter Stricker ◽

Lilian Krist ◽

Miriam Ortiz ◽

Thomas Reinhold ◽

...

Keyword(s):

Public Health ◽

Transmission Rate ◽

Latency Period ◽

Policy Decision ◽

Sensitivity Analyses ◽

Delphi Panel ◽

Time Step ◽

Seir Model ◽

Health Policy Decision ◽

The Impact

Abstract Aim To use a Delphi-panel-based assessment of the effectiveness of different non-pharmaceutical interventions (NPI) in order to retrospectively approximate and to prospectively predict the SARS-CoV-2 pandemic progression via a SEIR model (susceptible, exposed, infectious, removed). Methods We applied an evidence-educated Delphi-panel approach to elicit the impact of NPIs on the SARS-CoV-2 transmission rate R0 in Germany. Effectiveness was defined as the product of efficacy and compliance. A discrete, deterministic SEIR model with time step of 1 day, a latency period of 1.8 days, duration of infectiousness of 5 days, and a share of the total population of 15% assumed to be protected by immunity was developed in order to estimate the impact of selected NPI measures on the course of the pandemic. The model was populated with the Delphi-panel results and varied in sensitivity analyses. Results Efficacy and compliance estimates for the three most effective NPIs were as follows: test and isolate 49% (efficacy)/78% (compliance), keeping distance 42%/74%, personal protection masks (cloth masks or other face masks) 33%/79%. Applying all NPI effectiveness estimates to the SEIR model resulted in a valid replication of reported occurrence of the German SARS-CoV-2 pandemic. A combination of four NPIs at consented compliance rates might curb the CoViD-19 pandemic. Conclusion Employing an evidence-educated Delphi-panel approach can support SARS-CoV-2 modelling. Future curbing scenarios require a combination of NPIs. A Delphi-panel-based NPI assessment and modelling might support public health policy decision making by informing sequence and number of needed public health measures.

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Dynamic quarantine: a comparative analysis of the Chilean public health response to COVID-19

Epidemiology and Infection ◽

10.1017/s0950268820002678 ◽

2020 ◽

Vol 148 ◽

Author(s):

Gonzalo Grebe ◽

Javier A. Vélez ◽

Anton Tiutiunnyk ◽

Diego Aragón-Caqueo ◽

Javier Fernández-Salinas ◽

...

Keyword(s):

Public Health ◽

Comparative Analysis ◽

Transmission Rate ◽

National Level ◽

Metropolitan Region ◽

Public Health Response ◽

Country Level ◽

Health Response ◽

Primary Vector

Abstract In this study, an analysis of the Chilean public health response to mitigate the spread of COVID-19 is presented. The analysis is based on the daily transmission rate (DTR). The Chilean response has been based on dynamic quarantines, which are established, lifted or prolonged based on the percentage of infected individuals in the fundamental administrative sections, called communes. This analysis is performed at a national level, at the level of the Metropolitan Region (MR) and at the commune level in the MR according to whether the commune did or did not enter quarantine between late March and mid-May of 2020. The analysis shows a certain degree of efficacy in controlling the pandemic using the dynamic quarantine strategy. However, it also shows that apparent control has only been partially achieved to date. With this policy, the control of the DTR partially falls to 4%, where it settles, and the MR is the primary vector of infection at the country level. For this reason, we can conclude that the MR has not managed to control the disease, with variable results within its own territory.

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