Transmission Dynamics Of Coronavirus Disease 2019 Research Articles

UNDERSTANDING THE DYNAMICS OF COVID-19 TRANSMISSION IN PAMPANGA PHILIPPINES: MODELING WITH A SYSTEM OF DIFFERENTIAL EQUATIONS

This study introduced and solved a system of differential equations aimed at modeling the coronavirus disease 2019 (COVID-19) transmission dynamics in the province of Pampanga. Specifically, a Susceptible, Infected, Recovered, Deceased (SIRD) model was developed and built upon the foundational SIR model devised by Kermack and McKendrick in 1927.1 Various methods were employed to solve the model. Initially, the analytical solution for the rate of change of infected individuals over time was determined. Subsequently, model parameters were identified through an optimization process using the Microsoft Excel Solver. The Runge-Kutta fourth order (RK4) method, implemented in Scilab 6.1.1, was utilized to approximate the numerical solution for the rates of change of susceptible , recovered , and deceased over time. The findings underscored the significance of several parameters—namely, the transmission rate , removal rate (combining recovery and deceased rate ), the proportion of the infected population properly wearing face masks , the proportion disinfecting regularly , and the proportion practicing isolation or social distancing —in shaping the transmission dynamics of COVID-19 in Pampanga. The values of these model parameters reflect the effectiveness of governmental responses and actions in managing, controlling, and mitigating the spread of COVID-19, as well as the extent of public cooperation and compliance with COVID-19 directives and advisories.

Modelling the Impact of Vaccination on the Transmission Dynamics of COVID-19 in the Presence of Environmental Factor

The emergence of the Coronavirus Disease 2019 (COVID-19) pandemic, as reported by the World Health Organisation (WHO), originating from Wuhan, China in late 2019, poses a significant and formidable challenge to worldwide public health. As a result, more than 20 nations experienced the impact of this lethal illness. This research introduces a mathematical model, namely SEIQRV, which incorporates the SEIR model to analyse the ongoing COVID-19 epidemic in Nigeria. The model incorporates nonlinear forces of infection. The present model takes into account the many modes of transmission involved in the dynamics of infection, as well as the influence of the environmental reservoir on the dissemination of this particular illness to human populations. The establishment of the area in which the model is epidemiologically viable has been confirmed. A comprehensive numerical simulation of the model was performed using the data given by the Nigeria Centre for Disease Control (NCDC). The findings from our analysis and simulation suggest that administering an inadequate dose of vaccination will result in an elevated number of persons who are exposed to and infected by the virus. Additionally, this incomplete dosage is also likely to contribute to an increase in the concentration of the virus within the environmental reservoir. Hence, the need for effective vaccination with a zero-wane-off vaccine and compliance with vaccination dose, which can be achieved through educational campaign and public awareness of the need to be vaccinated, and not to be vaccinated alone but to complete the dose as incomplete dosage is dangerous to the community and the country at large. This will help a great deal in eradicating the spread of the COVID-19.

Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China

BackgroundIn 2022, Omicron outbreaks occurred at multiple sites in China. It is of great importance to track the incidence trends and transmission dynamics of coronavirus disease 2019 (COVID-19) to guide further interventions.MethodsGiven the population size, economic level and transport level similarities, two groups of outbreaks (Shanghai vs. Chengdu and Sanya vs. Beihai) were selected for analysis. We developed the SEAIQRD, ARIMA, and LSTM models to seek optimal modeling techniques for waves associated with the Omicron variant regarding data predictive performance and mechanism transmission dynamics, respectively. In addition, we quantitatively modeled the impacts of different combinations of more stringent interventions on the course of the epidemic through scenario analyses.ResultsThe best-performing LSTM model showed better prediction accuracy than the best-performing SEAIQRD and ARIMA models in most cases studied. The SEAIQRD model had an absolute advantage in exploring the transmission dynamics of the outbreaks. Regardless of the time to inflection point or the time to Rt curve below 1.0, Shanghai was later than Chengdu (day 46 vs. day 12/day 54 vs. day 14), and Sanya was later than Beihai (day 16 vs. day 12/day 20 vs. day 16). Regardless of the number of peak cases or the cumulative number of infections, Shanghai was higher than Chengdu (34,350 vs. 188/623,870 vs. 2,181), and Sanya was higher than Beihai (1,105 vs. 203/16,289 vs. 3,184). Scenario analyses suggested that upgrading control level in advance, while increasing the index decline rate and quarantine rate, were of great significance for shortening the time to peak and Rt below 1.0, as well as reducing the number of peak cases and final affected population.ConclusionsThe LSTM model has great potential for predicting the prevalence of Omicron outbreaks, whereas the SEAIQRD model is highly effective in revealing their internal transmission mechanisms. We recommended the use of joint interventions to contain the spread of the virus.

Co-infection dynamics of COVID-19 and HIV/AIDS

Although there are many results that can be used to treat and prevent Coronavirus Disease 2019 (COVID-19) and Human Immunodeficiency Virus (HIV), these diseases continue to be public health concerns and cause socioeconomic consequences. Following compromised immunity, COVID-19 is considered to be a challenge for people with HIV. People with advanced HIV are considered a vulnerable population at high risk in several case studies that discuss COVID-19 and HIV co-infection. As there is no cure for HIV and there is a chance of contracting COVID-19 again, co-infection continues to pose a problem. The purpose of this study is to investigate the impact of intervention strategies and identify the role of different parameters in risking people living with HIV to death when they get infected with COVID-19. This is achieved through the development and rigorous analysis of a mathematical model that considers a population at risk of death due to COVID-19 and HIV. The model formulation provides a detailed explanation of the transmission dynamics of COVID-19 and HIV co-infection. The solution’s invariant region, positivity, and boundedness were established. The reproduction numbers of the sub-models and the co-infection model were determined. The existence and stability of equilibria, including backward bifurcation for the COVID-19 sub-model, were examined. The epidemiological significance of backward bifurcation is that the condition {mathscr {R}}_0 less than 1 for eliminating COVID-19, though necessary, is no longer sufficient. Parametric estimation and curve fitting were performed based on data from Ethiopia. Numerical simulations were employed to support and clarify the analytical findings and to show some parameter effects on COVID-19 and HIV co-infection. Accordingly, the simulations indicated that parameters gamma _c, gamma _h, epsilon, and kappa, related to HIV patients’ exposure to other diseases and the increase in infectiousness, have a positive role in increasing the number of co-infections. On the other hand, an increase in COVID-19 vaccination (xi) shows the suppression of co-infection cases. In addition, treating co-infected individuals for COVID-19, increasing treatment rates alpha and varphi, reduces the death risk of HIV-infected individuals due to the co-infection burden. It was implied that improving vaccine delivery programs and other medical interventions have important contributions to lowering the risk of COVID-19 infection-related fatalities in HIV patients.

Spatial and temporal dynamics of COVID-19 with nonlocal dispersal in heterogeneous environment: Modeling, analysis and simulation

This paper proposes an infectious disease dynamics model with spatial heterogeneity to study the joint impact of nonlocal dispersal and vaccine on the transmission dynamics of COVID-19 (Corona Virus Disease 2019). Overcoming the difficulty of non-compactness caused by non-local operators, the functional expression of the next generation operator R is obtained by using the renewal equation. And then, the basic reproduction number R0 of the proposed model is obtained, i.e., the spectral radius of the next generation operator R. In terms of theoretical analysis, we prove that the global asymptotic stability of the disease-free steady state when R0<1; Using the uniform persistence theory of point dissipative systems, we prove the system is uniformly persistent and has at least one positive steady state when R0>1. We verify the theoretical results and study the influence of related factors on the transmission of COVID-19 pandemic in numerical simulation part. Finally, our numerical results show that: (1) Improving the coverage rate of COVID-19 complete vaccine and the vaccine efficiency is one of the optimal measures to effectively prevent and control the spread of COVID-19 under the current medical conditions; (2) The influence of the form of nonlocal dispersal kernel function and the virus in environment on the transmission of COVID-19 cannot be ignored Therefore, targeted prevention and control measures should be taken for the dispersal of the virus in urban areas and in the process of cold chain transportation to prevent COVID-19 from spreading to rural areas with relatively backward medical conditions.

Epidemiological characteristics and transmission dynamics of the COVID-19 outbreak in Hohhot, China: a time-varying SQEIAHR model analysis.

On September 28, 2022, the first case of Omicron subvariant BF.7 was discovered among coronavirus disease 2019 (COVID-19) infections in Hohhot, China, and then the epidemic broke out on a large scale during the National Day holiday. It is imminently necessary to construct a mathematical model to investigate the transmission dynamics of COVID-19 in Hohhot. In this study, we first investigated the epidemiological characteristics of COVID-19 cases in Hohhot, including the spatiotemporal distribution and sociodemographic distribution. Then, we proposed a time-varying Susceptible-Quarantined Susceptible-Exposed-Quarantined Exposed-Infected-Asymptomatic-Hospitalized-Removed (SQEIAHR) model to derive the epidemic curves. The next-generation matrix method was used to calculate the effective reproduction number (Re). Finally, we explored the effects of higher stringency measures on the development of the epidemic through scenario analysis. Of the 4,889 positive infected cases, the vast majority were asymptomatic and mild, mainly concentrated in central areas such as Xincheng District. People in the 30-59 age group primarily were affected by the current outbreak, accounting for 53.74%, but females and males were almost equally affected (1.03:1). Community screening (35.70%) and centralized isolation screening (26.28%) were the main ways to identify positive infected cases. Our model predicted the peak of the epidemic on October 6, 2022, the dynamic zero-COVID date on October 15, 2022, a number of peak cases of 629, and a cumulative number of infections of 4,963 (95% confidential interval (95%CI): 4,692 ~ 5,267), all four of which were highly consistent with the actual situation in Hohhot. Early in the outbreak, the basic reproduction number (R0) was approximately 7.01 (95%CI: 6.93 ~ 7.09), and then Re declined sharply to below 1.0 on October 6, 2022. Scenario analysis of higher stringency measures showed the importance of decreasing the transmission rate and increasing the quarantine rate to shorten the time to peak, dynamic zero-COVID and an Re below 1.0, as well as to reduce the number of peak cases and final affected population. Our model was effective in predicting the epidemic trends of COVID-19, and the implementation of a more stringent combination of measures was indispensable in containing the spread of the virus.

Transmission dynamics and baseline epidemiological parameter estimates of Coronavirus disease 2019 pre-vaccination: Davao City, Philippines.

The Coronavirus disease 2019 (COVID-19) has exposed many systemic vulnerabilities in many countries' health system, disaster preparedness, and adequate response capabilities. With the early lack of data and information about the virus and the many differing local-specific factors contributing to its transmission, managing its spread had been challenging. The current work presents a modified Susceptible-Exposed-Infectious-Recovered compartmental model incorporating intervention protocols during different community quarantine periods. The COVID-19 reported cases before the vaccine rollout in Davao City, Philippines, are utilized to obtain baseline values for key epidemiologic model parameters. The probable secondary infections (i.e., time-varying reproduction number) among other epidemiological indicators were computed. Results show that the cases in Davao City were driven by the transmission rates, positivity proportion, latency period, and the number of severely symptomatic patients. This paper provides qualitative insights into the transmission dynamics of COVID-19 along with the government's implemented intervention protocols. Furthermore, this modeling framework could be used for decision support, policy making, and system development for the current and future pandemics.

Mathematical Models Used to Assess the COVID-19 Outbreak and Preventive Measures

Background: Mathematical modelling is one of the most powerful methods for exploring the diverse range of intervention strategies, determining the fundamental reproduction rate, and forecasting disease spread. This paper reviews the mathematical models that were utilized to investigate coronavirus disease 2019 (COVID-19) transmission dynamics. Methods: We looked through four electronic databases for research that evaluated the transmission dynamics of COVID-19 using a mathematical modeling strategy based on compartmental models. Research describing COVID-19 transmission utilizing compartmental models and published in English in preprints and peer-reviewed publications between December 30, 2019 and May 25, 2020 was eligible for inclusion. This limitation period of time was chosen because it’s when the disease emerged and peaked in many countries worldwide. Results: A total of 46 studies that used compartmental models to explain COVID-19 transmission were included in the review. Six studies employed the age-structured SEIR model, making up the majority of studies’ (37/46) use of the Susceptible-Exposed-Infected-Recovered (SEIR) framework. In one of the eight investigations that used SIR, an age-structured model was used. One study evaluated COVID-19 transmission using the generalized SIRS model. To anticipate the onset of the disease, 36 of the 46 studies calculated the basic reproduction number, and 5 estimated the effective reproduction number. Conclusion: Modelling studies applied generalised and age-structure models to assess the transmission and control of the COVID-19, suggesting that combined mitigations have a substantial impact on disease transmission

Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction.

U.S. school closures due to the coronavirus disease 2019 (COVID-19) pandemic led to extended periods of remote learning and social and economic impact on families. Uncertainty about virus dynamics made it difficult for school districts to develop mitigation plans that all stakeholders consider to be safe. We developed an agent-based model of infection dynamics and preventive mitigation designed as a conceptual tool to give school districts basic insights into their options, and to provide optimal flexibility and computational ease as COVID-19 science rapidly evolved early in the pandemic. Elements included distancing, health behaviors, surveillance and symptomatic testing, daily symptom and exposure screening, quarantine policies, and vaccination. Model elements were designed to be updated as the pandemic and scientific knowledge evolve. An online interface enables school districts and their implementation partners to explore the effects of interventions on outcomes of interest to states and localities, under a variety of plausible epidemiological and policy assumptions. The model shows infection dynamics that school districts should consider. For example, under default assumptions, secondary infection rates and school attendance are substantially affected by surveillance testing protocols, vaccination rates, class sizes, and effectiveness of safety education. Our model helps policymakers consider how mitigation options and the dynamics of school infection risks affect outcomes of interest. The model was designed in a period of considerable uncertainty and rapidly evolving science. It had practical use early in the pandemic to surface dynamics for school districts and to enable manipulation of parameters as well as rapid update in response to changes in epidemiological conditions and scientific information about COVID-19 transmission dynamics, testing and vaccination resources, and reliability of mitigation strategies.

Mathematical Modeling of COVID-19 Transmission and Intervention in South Korea: A Review of Literature

South Korea implemented interventions to curb the spread of the novel coronavirus disease 2019 (COVID-19) pandemic with discovery of the first case in early 2020. Mathematical modeling designed to reflect the dynamics of disease transmission has been shown to be an important tool for responding to COVID-19. This study aimed to review publications on the structure, method, and role of mathematical models focusing on COVID-19 transmission dynamics in Korea. In total, 42 papers published between August 7, 2020 and August 21, 2022 were studied and reviewed. This study highlights the construction and utilization of mathematical models to help craft strategies for predicting the course of an epidemic and evaluating the effectiveness of control strategies. Despite the limitations caused by a lack of available epidemiological and surveillance data, modeling studies could contribute to providing scientific evidence for policymaking by simulating various scenarios.

High-Resolution Data on Human Behavior for Effective COVID-19 Policy-Making - Wuhan City, Hubei Province, China, January 1-February 29, 2020.

High-resolution data is essential for understanding the complexity of the relationship between the spread of coronavirus disease 2019 (COVID-19), resident behavior, and interventions, which could be used to inform policy responses for future prevention and control. We obtained high-resolution human mobility data and epidemiological data at the community level. We propose a metapopulation Susceptible-Exposed-Presymptomatic-Infectious-Removal (SEPIR) compartment model to utilize the available data and explore the internal driving forces of COVID-19 transmission dynamics in the city of Wuhan. Additionally, we will assess the effectiveness of the interventions implemented in the smallest administrative units (subdistricts) during the lockdown. In the Wuhan epidemic of March 2020, intra-subdistrict transmission caused 7.6 times more infections than inter-subdistrict transmission. After the city was closed, this ratio increased to 199 times. The main transmission path was dominated by population activity during peak evening hours. Restricting the movement of people within cities is an essential measure for controlling the spread of COVID-19. However, it is difficult to contain intra-street transmission solely through city-wide mobility restriction policies. This can only be accomplished by quarantining communities or buildings with confirmed cases, and conducting mass nucleic acid testing and enforcing strict isolation protocols for close contacts.

Modified SIR model for COVID-19 transmission dynamics: Simulation with case study of UK, US and India

Corona virus disease 2019 (COVID-19) is an infectious disease and has spread over more than 200 countries since its outbreak in December 2019. This pandemic has posed the greatest threat to global public health and seems to have changing characteristics with altering variants, hence various epidemiological and statistical models are getting developed to predict the infection spread, mortality rate and calibrating various impacting factors. But the aysmptomatic patient counts and demographical factors needs to be considered in model evaluation. Here we have proposed a new seven compartmental model, Susceptible- Exposed- Infected–Asymptomatic–Quarantined–Fatal–Recovered (SEIAQFR) which is based on classical Susceptible-Infected-Recovered (SIR) model dynamic of infectious disease, and considered factors like asymptomatic transmission and quarantine of patients. We have taken UK, US and India as a case study for model evaluation purpose. In our analysis, it is found that the Reproductive Rate (R0) of the disease is dynamic over a long period and provides better results in model performance (>0.98 R-square score) when model is fitted across smaller time period. On an average 40%−50% cases are asymptomatic and have contributed to model accuracy. The model is employed to show accuracy in correspondence with different geographic data in both wave of disease spread. Different disease spreading factors like infection rate, recovery rate and mortality rate are well analyzed with best fit of real world data. Performance evaluation of this model has achieved good R-Square score which is 0.95−0.99 for infection prediction and 0.90−0.99 for death prediction and an average 1%−5% MAPE in different wave of the disease in UK, US and India.

SCoVMod – a spatially explicit mobility and deprivation adjusted model of first wave COVID-19 transmission dynamics

Background: Mobility restrictions prevent the spread of infections to disease-free areas, and early in the coronavirus disease 2019 (COVID-19) pandemic, most countries imposed severe restrictions on mobility as soon as it was clear that containment of local outbreaks was insufficient to control spread. These restrictions have adverse impacts on the economy and other aspects of human health, and it is important to quantify their impact for evaluating their future value. Methods: Here we develop Scotland Coronavirus transmission Model (SCoVMod), a model for COVID-19 in Scotland, which presents unusual challenges because of its diverse geography and population conditions. Our fitted model captures spatio-temporal patterns of mortality in the first phase of the epidemic to a fine geographical scale. Results: We find that lockdown restrictions reduced transmission rates down to an estimated 12\\% of its pre-lockdown rate. We show that, while the timing of COVID-19 restrictions influences the role of the transmission rate on the number of COVID-related deaths, early reduction in long distance movements does not. However, poor health associated with deprivation has a considerable association with mortality; the Council Area (CA) with the greatest health-related deprivation was found to have a mortality rate 2.45 times greater than the CA with the lowest health-related deprivation considering all deaths occurring outside of carehomes. Conclusions: We find that in even an early epidemic with poor case ascertainment, a useful spatially explicit model can be fit with meaningful parameters based on the spatio-temporal distribution of death counts. Our simple approach is useful to strategically examine trade-offs between travel related restrictions and physical distancing, and the effect of deprivation-related factors on outcomes.

Post pandemic fatigue: what are effective strategies?

Recurrent updates in non-pharmaceutical interventions (NPIs) aim to control successive waves of the coronavirus disease 2019 (COVID-19) but are often met with low adherence by the public. This study evaluated the effectiveness of gathering restrictions and quarantine policies based on a modified Susceptible-Exposed-Infectious-Hospitalized-Recovered (SEIHR) model by incorporating cross-boundary travellers with or without quarantine to study the transmission dynamics of COVID-19 with data spanning a nine-month period during 2020 in Hong Kong. The asymptotic stability of equilibria reveals that the model exhibits the phenomenon of backward bifurcation, which in this study is a co-existence between a stable disease-free equilibrium (DFE) and an endemic equilibrium (EE). Even if the basic reproduction number ({mathscr {R}}_{0}) is less than unity, this disease cannot be eliminated. The effect of each parameter on the overall dynamics was assessed using Partial Rank Correlation Coefficients (PRCCs). Transmission rates (i.e., beta _1 and beta _2), effective contact ratio a_6 between symptomatic individuals and quarantined people, and transfer rate theta _3 related to infection during quarantine were identified to be the most sensitive parameters. The effective contact ratios between the infectors and susceptible individuals in late July were found to be over twice as high as that in March of 2020, reflecting pandemic fatigue and the potential existence of infection during quarantine.

Clinical Time Delay Distributions of COVID-19 in 2020–2022 in the Republic of Korea: Inferences from a Nationwide Database Analysis

Epidemiological distributions of the coronavirus disease 2019 (COVID-19), including the intervals from symptom onset to diagnosis, reporting, or death, are important for developing effective disease-control strategies. COVID-19 case data (from 19 January 2020 to 10 January 2022) from a national database maintained by the Korea Disease Control and Prevention Agency and the Central Disease Control Headquarters were analyzed. A joint Bayesian subnational model with partial pooling was used and yielded probability distribution models of key epidemiological distributions in Korea. Serial intervals from before and during the Delta variant’s predominance were estimated. Although the mean symptom-onset-to-report interval was 3.2 days at the national level, it varied across different regions (2.9–4.0 days). Gamma distribution showed the best fit for the onset-to-death interval (with heterogeneity in age, sex, and comorbidities) and the reporting-to-death interval. Log-normal distribution was optimal for ascertaining the onset-to-diagnosis and onset-to-report intervals. Serial interval (days) was shorter before the Delta variant-induced outbreaks than during the Delta variant’s predominance (4.4 vs. 5.2 days), indicating the higher transmission potential of the Delta variant. The identified heterogeneity in region-, age-, sex-, and period-based distributions of the transmission dynamics of COVID-19 will facilitate the development of effective interventions and disease-control strategies.

SCoVMod - a spatially explicit mobility and deprivation adjusted model of first wave COVID-19 transmission dynamics.

Background: Mobility restrictions prevent the spread of infections to disease-free areas, and early in the coronavirus disease 2019 (COVID-19) pandemic, most countries imposed severe restrictions on mobility as soon as it was clear that containment of local outbreaks was insufficient to control spread. These restrictions have adverse impacts on the economy and other aspects of human health, and it is important to quantify their impact for evaluating their future value. Methods: Here we develop Scotland Coronavirus transmission Model (SCoVMod), a model for COVID-19 in Scotland, which presents unusual challenges because of its diverse geography and population conditions. Our fitted model captures spatio-temporal patterns of mortality in the first phase of the epidemic to a fine geographical scale. Results: We find that lockdown restrictions reduced transmission rates down to an estimated 12\% of its pre-lockdown rate. We show that, while the timing of COVID-19 restrictions influences the role of the transmission rate on the number of COVID-related deaths, early reduction in long distance movements does not. However, poor health associated with deprivation has a considerable association with mortality; the Council Area (CA) with the greatest health-related deprivation was found to have a mortality rate 2.45 times greater than the CA with the lowest health-related deprivation considering all deaths occurring outside of carehomes. Conclusions: We find that in even an early epidemic with poor case ascertainment, a useful spatially explicit model can be fit with meaningful parameters based on the spatio-temporal distribution of death counts. Our simple approach is useful to strategically examine trade-offs between travel related restrictions and physical distancing, and the effect of deprivation-related factors on outcomes.

Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology

As a cost-effective and objective population-wide surveillance tool, wastewater-based epidemiology (WBE) has been widely implemented worldwide to monitor the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentration in wastewater. However, viral concentrations or loads in wastewater often correlate poorly with clinical case numbers. To date, there is no reliable method to back-estimate the coronavirus disease 2019 (COVID-19) case numbers from SARS-CoV-2 concentrations in wastewater. This greatly limits WBE in achieving its full potential in monitoring the unfolding pandemic. The exponentially growing SARS-CoV-2 WBE dataset, on the other hand, offers an opportunity to develop data-driven models for the estimation of COVID-19 case numbers (both incidence and prevalence) and transmission dynamics (effective reproduction rate). This study developed artificial neural network (ANN) models by innovatively expanding a conventional WBE dataset to include catchment, weather, clinical testing coverage and vaccination rate. The ANN models were trained and evaluated with a comprehensive state-wide wastewater monitoring dataset from Utah, USA during May 2020 to December 2021. In diverse sewer catchments, ANN models were found to accurately estimate the COVID-19 prevalence and incidence rates, with excellent precision for prevalence rates. Also, an ANN model was developed to estimate the effective reproduction number from both wastewater data and other pertinent factors affecting viral transmission and pandemic dynamics. The established ANN model was successfully validated for its transferability to other states or countries using the WBE dataset from Wisconsin, USA.

The Second Wave of COVID-19 in South and Southeast Asia and the Effects of Vaccination.

Background: By February 2021, the overall impact of coronavirus disease 2019 (COVID-19) in South and Southeast Asia was relatively mild. Surprisingly, in early April 2021, the second wave significantly impacted the population and garnered widespread international attention.Methods: This study focused on the nine countries with the highest cumulative deaths from the disease as of August 17, 2021. We look at COVID-19 transmission dynamics in South and Southeast Asia using the reported death data, which fits a mathematical model with a time-varying transmission rate.Results: We estimated the transmission rate, infection fatality rate (IFR), infection attack rate (IAR), and the effects of vaccination in the nine countries in South and Southeast Asia. Our study suggested that the IAR is still low in most countries, and increased vaccination is required to prevent future waves.Conclusion: Implementing non-pharmacological interventions (NPIs) could have helped South and Southeast Asia keep COVID-19 under control in 2020, as demonstrated in our estimated low-transmission rate. We believe that the emergence of the new Delta variant, social unrest, and migrant workers could have triggered the second wave of COVID-19.

Incidence and transmission dynamics of Coronavirus Disease 2019 (COVID-19) among fresh recruits in a Training Establishment of Indian Armed Forces: An observational study

Introduction: In times of COVID-19, when all institutions remained closed even post lock-down, the Airmen Training School of Indian Air Force proceeded with the recruitment of airmen. The present study intends to explore the incidence and transmission dynamics of COVID-19 among these freshly recruits who were quarantined in barracks with each barrack serving as one cohort. Material and Methods: In an observational study, 2978 male Air Force recruits, between the age group of 18-22 years, in two batches between 1st to 31st July 2020, were followed up in the quarantine period post recruitment. Since the quarantine was unconventional in terms of being in barracks, special and unique precautions such as repeated screening, delegation of specific toilets per billets, three ply mask distributions, Bio-Medical Waste Disposal etc were paid utmost attention. The COVID 19 cases were treated at the in-house isolation facility and the contacts with the symptomatics tested with RT-PCR or RAT before release from quarantine. All individuals were monitored with active surveillance and the data was meticulously maintained with zero attrition. Incidence, Secondary Attack Rate (SAR), demographic characteristics and others aspects of transmission dynamics were analyzed. Results: The incidence and SAR was observed to be 8.1% (71 primary cases) and 15.59% (170 secondary cases). 64.7% of cases were symptomatic with an average of 15 days of hospitalization. The rate positivity of testing with RTPCR or RAT was 11.44%. Severity was more marked in the first batch than the second batch of recruits. The overall R0 was found to be 2.39. Conclusion: The results of the study revealed some interesting characteristics of COVID-19 transmission dynamics. The cumulative incidence was found to be higher than national average. The variations between the recruits from different part of the country and the two batches could be attributed to the travel distance and mode of conveyance. The study also validates the mild nature of the infection in young adults. Fair conclusion on the implication of good quarantine and isolation measures and regular surveillance for symptoms for early diagnosis has also been compellingly indicated from these observations.

Predicting the effective reproduction number of COVID-19: inference using human mobility, temperature, and risk awareness

ObjectivesThe effective reproduction number (Rt) has been critical for assessing the effectiveness of countermeasures during the coronavirus disease 2019 (COVID-19) pandemic. Conventional methods using reported incidences are unable to provide timely Rt data due to the delay from infection to reporting. Our study aimed to develop a framework for predicting Rt in real time, using timely accessible data — i.e. human mobility, temperature, and risk awareness. MethodsA linear regression model to predict Rt was designed and embedded in the renewal process. Four prefectures of Japan with high incidences in the first wave were selected for model fitting and validation. Predictive performance was assessed by comparing the observed and predicted incidences using cross-validation, and by testing on a separate dataset in two other prefectures with distinct geographical settings from the four studied prefectures. ResultsThe predicted mean values of Rt and 95% uncertainty intervals followed the overall trends for incidence, while predictive performance was diminished when Rt changed abruptly, potentially due to superspreading events or when stringent countermeasures were implemented. ConclusionsThe described model can potentially be used for monitoring the transmission dynamics of COVID-19 ahead of the formal estimates, subject to delay, providing essential information for timely planning and assessment of countermeasures.