Prediction of the COVID-19 transmission: a case study of Pakistan.

The world has suffered a lot from COVID-19 and is still on the verge of a new outbreak. The infected regions of coronavirus have been classified into four categories: SIRD model, (1) suspected, (2) infected, (3) recovered, and (4) deaths, where the COVID-19 transmission is evaluated using a stochastic model. A study in Pakistan modeled COVID-19 data using stochastic models like PRM and NBR. The findings were evaluated based on these models, as the country faces its third wave of the virus. Our study predicts COVID-19 casualties in Pakistan using a count data model. We've used a Poisson process, SIRD-type framework, and a stochastic model to find the solution. We took data from NCOC (National Command and Operation Center) website to choose the best prediction model based on all provinces of Pakistan, On the values of log L and AIC criteria. The best model among PRM and NBR is NBR because when over-dispersion happens; NBR is the best model for modelling the total suspected, infected, and recovered COVID-19 occurrences in Pakistan as it has the maximum log L and smallest AIC of the other count regression model. It was also observed that the active and critical cases positively and significantly affect COVID-19-related deaths in Pakistan using the NBR model.

Multiregional, multi-industry impacts of fairness on pandemic policies.

The health and economic crisis caused by the COVID-19 pandemic highlights the necessity for a deeper understanding and investigation of state- and industry-level mitigation policies. While different control strategies in the early stages, such as lockdowns and school and business closures, have helped decrease the number of infections, these strategies have had an adverse economic impact on businesses and some controversial impacts on social justice. Therefore, optimal timing and scale of closure and reopening strategies are required to prevent both different waves of the pandemic and the negative socioeconomic impact of control strategies. This article proposes a novel multiobjective mixed-integer linear programming formulation, which results in the optimal timing of closure and reopening of states and industries in each. The three objectives being pursued include: (i) the epidemiological impact of the pandemic in terms of the percentage of the infected population; (ii) the social vulnerability index of the pandemic policy based on the vulnerability of communities to getting infected, and for losing their job; and (iii) the economic impact of the pandemic based on the inoperability of industries in each state. The proposed model is implemented on a dataset that includes 50 states, the District of Columbia, and 19 industries in the United States. The Pareto-optimal solutions suggest that for any control decision (state and industry closure or reopening), the economic impact and the epidemiological impact change in the opposite direction.

The Impact of Restricting Community Activities on COVID-19 Transmission: A Case Study in Sumatra Island, Indonesia

Sumatra Island is the third largest island with the second largest population in Indonesia which has the following eight provinces: Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu and Lampung. The connectivity of these eight provinces in the economic field is very strong. This encourages high mobility between these provinces. During this Covid-19 pandemic, the high mobility between provinces affects the level of spread of Covid-19 on the island of Sumatra. The central government ordered local governments to implement a community activity restriction program called PPKM. In this article, a study is conducted on the impact of the PKKM program on the spread of Covid 19 on the island of Sumatra, Indonesia. The spread of Covid-19 is modeled using the Susceptible-Infected-Recovered-Death (SIRD) model which considers the mobility factor of the population. The model parameters were estimated using Approximate Bayesian Computation (ABC). The results of the study using this model show that the application of PKKM in several provinces in Sumatra can reduce the level of spread of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A simple planning problem for COVID-19 lockdown: a dynamic programming approach.

A large number of recent studies consider a compartmental SIR model to study optimal control policies aimed at containing the diffusion of COVID-19 while minimizing the economic costs of preventive measures. Such problems are non-convex and standard results need not to hold. We use a Dynamic Programming approach and prove some continuity properties of the value function of the associated optimization problem. We study the corresponding Hamilton-Jacobi-Bellman equation and show that the value function solves it in the viscosity sense. Finally, we discuss some optimality conditions. Our paper represents a first contribution towards a complete analysis of non-convex dynamic optimization problems, within a Dynamic Programming approach.

Nonlinear control of Covid-19 pandemic based on the SIRD model

In the last month of 2019, a new version of Corona disease was observed in Wuhan (China) which is known as Covid-19. Several models have been proposed to predict disease treatment. The SIR model is considered one of the simplest models for the prediction of pandemic disease. This means susceptible (S), infected (I), and recovered (R) populations. The SIRD model is yet another method that includes one more equation, i.e., the number of deaths (D). This paper proposed a control law for the first time to prevent the progression of the disease. The proposed control law is based on the SIRD model and uses the feedback linearization method for the Covid-19 nonlinear model. The goal of control in this model is to reduce the number of people infected with the Covid-19 and the number of deaths due to the disease. Delay in treatment of infected people and percentage of people who should be treated are investigated as two important parameters. The results show that with the treatment of infected people in the first weeks, the number of people infected decreases by 96.3% and the number of deaths by 93.6% © 2022 IEEE.

Modeling social, economic, and health perspectives for optimal pandemic policy decision-making.

While different control strategies in the early stages of the COVID-19 pandemic have helped decrease the number of infections, these strategies have had an adverse economic impact on businesses. Therefore, optimal timing and scale of closure and reopening strategies are required to prevent both different waves of the pandemic and the negative economic impact of control strategies. This paper proposes a novel multi-objective mixed-integer linear programming (MOMILP) formulation, which results in the optimal timing of closure and reopening of states and industries in each state to mitigate the economic and epidemiological impact of a pandemic. The three objectives being pursued include: (i) the epidemiological impact, (ii) the economic impact on the local businesses, and (iii) the economic impact on the trades between industries. The proposed model is implemented on a dataset that includes 11 states, the District of Columbia, and 19 industries in the US. The solved by augmented ε-constraint approach is used to solve the multi-objective model, and a final strategy is selected from the set of Pareto-optimal solutions based on the least cubic distance of the solution from the optimal value of each objective. The Pareto-optimal solutions suggest that for any control decision (state and industry closure or reopening), the economic impact and the epidemiological impact change in the opposite direction, and it is more effective to close most states while keeping the majority of industries open during the planning horizon.

Theory-Guided Bayesian Analysis for Modeling Impact of COVID-19 on Gross Domestic Product

GDP or Gross Domestic Product is a key indicator of economic status, which provides an omni-comprehensive measure of the wealth of a country or a state. With the sudden proliferation of novel coronavirus disease (COVID-19), there has been increasing interest in forecasting GDP, since this may be severely impacted by the various pandemic control measures imposed in recent days. An accurate forecast of GDP can extensively help in putting forth right administrative measures while ensuring minimum disruption in economy. Though the recent researches focus on various machine learning-based data-driven models for this purpose, these primarily analyze the change in observed GDP data without explicitly modeling the pandemic impact. We address this issue by proposing a novel approach that incorporates epidemiological insights into Bayesian network-based predictive analytics to account for the influence of COVID-19 development on the GDP. Rigorous experimentation on state-level and country-level datasets of India demonstrates that a judicious combination of theoretical and data-driven models can substantially improve GDP forecast performance. Our model produces an average prediction error of 0.002% and outperforms several state-of-the-art techniques with a large margin. © 2022 IEEE.

A Dynamic Model for Analyzing the Public Health Policy of the Mexican Government During the COVID-19 Pandemic

The COVID-19 Pandemic has been one of the most significant health problems in the world. Several academic studies focus on health consequences. However, only a few studies have paid attention to analyzing government strategies as general public guidelines. In this work, we use a Susceptible-Infected-Recovered-Deceased (SIRD) model to assess infection rate, mortality rate, and the effects of the Mexican government intervention during the three waves of the COVID-19 pandemic. We carry out Bayesian inferences on the proposed model using a Robust Adaptive Metropolis (RAM) algorithm. Essentially, the proposed methodology allows appreciating the effects of quarantine on the three pandemic wave's mortality rate. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 Outbreak Prediction in Indonesia Based on Machine Learning and SIRD-BasedHybrid Methods

This paper aims to forecast and analyze the spread of COVID-19 outbreak in Indonesia by applying machine learning and hybrid approaches. We show the performance of each method, an ensemble-support vector regression (ensemble-SVR), a genetic algorithm and an SIRD model (GA-SIRD) and an extended Kalman filter, a genetic algorithm and an extended Kalman filter (EKF-GA-SIRD), in obtaining the prediction of the outbreak. The GA-SIRD model is built based on the data availability and is enhanced by employing an extended Kalman filter to better predict the spread of the outbreak. Without considering the epidemic model, the ensemble SVR can provide a higher accuracy compare to the two hybrid approaches in the case of short-term forecasting. Furthermore, the EKF-GA-SIRD can better adapt to the extreme change and shows a better performance than the GA-SIRD. © 2021.

Deep learning application for real-time prediction of COVID-19 outbreak with susceptible-infected-recovered-deceased model

Due to the complex nature of a pandemic such as COVID-19, forecasting how it would behave is difficult, but it is indeed of utmost necessity. Furthermore, adapting predictive models to different data sets obtained from different countries and areas is necessary, as it can provide a wider view of the global pandemic situation and more information on how models can be improved. Therefore, we combine here the long-short-term memory (LSTM) model and the traditional susceptible-infected-recovered-deceased (SIRD) model for the COVID-19 prediction task in Ho Chi Minh City, Vietnam. In particular, LSTM shows its strength in processing and making accurate numerical predictions on a large set of historical input. Following the SIRD model, the whole population is divided into 4 states (S), (I), (R), and (D), and the changes from one state to another are governed by a parameter set. By assessing the numerical output and the corresponding parameter set, we could reveal more insights about the root causes of the changes. The predictive model updates every 10 days to produce an output that is closest to reality. In general, such a combination delivers transparent, accurate, and up-to-date predictions for human experts, which is important for research on COVID-19. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

Efficient social distancing during the COVID-19 pandemic: Integrating economic and public health considerations.

Although social distancing can effectively contain the spread of infectious diseases by reducing social interactions, it may have economic effects. Crises such as the COVID-19 pandemic create dilemmas for policymakers because the long-term implementation of restrictive social distancing policies may cause massive economic damage and ultimately harm healthcare systems. This paper proposes an epidemic control framework that policymakers can use as a data-driven decision support tool for setting efficient social distancing targets. The framework addresses three aspects of the COVID-19 pandemic that are related to social distancing or community mobility data: modeling, financial implications, and policy-making. Thus, we explore the COVID-19 pandemic and concurrent economic situation as functions of historical pandemic data and mobility control. This approach allows us to formulate an efficient social distancing policy as a stochastic feedback control problem that minimizes the aggregated risks of disease transmission and economic volatility. We further demonstrate the use of a deep learning algorithm to solve this control problem. Finally, by applying our framework to U.S. data, we empirically examine the efficiency of the U.S. social distancing policy.

Trade-off between job losses and the spread of COVID-19 in Japan.

This paper quantitatively analyzes the trade-off between job losses and the spread of COVID-19 in Japan. We derive an empirical specification from the social planner's resource constraint under the susceptible, infected, recovered, and deaths (SIRD) model and estimate how job losses and the case growth rate are related to people's mobility using the Japanese prefecture-level panel data on confirmed cases, involuntary job losses, people's mobility, and teleworkability. Our findings are summarized as follows. First, we find that a decrease in mobility driven by containment policies is associated with an increase in involuntary job separations, but the high teleworkability mitigates the negative effect of decreased mobility on job losses. Second, estimating how the case growth is related to people's mobility and past cases, we find that the case growth rate is positively related to an increase in people's mobility but negatively associated with past confirmed cases. Third, using these estimates, we provide a quantitative analysis of the trade-off between job losses and the number of confirmed cases. Taking Tokyo in July 2020 as a benchmark, we find that the cost of saving 1 job per month is 2.3 more confirmed cases per month in the short run of 1 month. When we consider a trade-off for 3 months from July to September of 2020, protecting 1 job per month requires 6.6 more confirmed cases per month. Therefore, the trade-off becomes worse substantially in the longer run of 3 months, reflecting the exponential case growth when the people's mobility is high.

Optimal timing of non-pharmaceutical interventions during an epidemic.

In response to the recent outbreak of the SARS-CoV-2 virus governments have aimed to reduce the virus's spread through, inter alia, non-pharmaceutical intervention. We address the question when such measures should be implemented and, once implemented, when to remove them. These issues are viewed through a real-options lens and we develop an SIRD-like continuous-time Markov chain model to analyze a sequence of options: the option to intervene and introduce measures and, after intervention has started, the option to remove these. Measures can be imposed multiple times. We implement our model using estimates from empirical studies and, under fairly general assumptions, our main conclusions are that: (1) measures should be put in place not long after the first infections occur; (2) if the epidemic is discovered when there are many infected individuals already, then it is optimal never to introduce measures; (3) once the decision to introduce measures has been taken, these should stay in place until the number of susceptible or infected members of the population is close to zero; (4) it is never optimal to introduce a tier system to phase-in measures but it is optimal to use a tier system to phase-out measures; (5) a more infectious variant may reduce the duration of measures being in place; (6) the risk of infections being brought in by travelers should be curbed even when no other measures are in place. These results are robust to several variations of our base-case model.

Analysis of SARS-CoV-2 High Infection Spread in India Based on SIRD Model and Structural Insights

SARS-CoV-2, the causative agent of COVID-19 first emerged in Wuhan, China, in 2019. With antigen drift of the RNA beta-coronavirus, a number of variants have appeared, especially, B.1.617 variant, which rapidly spread throughout India and caused a devastating global pandemic. However, the high infectious mechanism is still under discussion. In this paper, the Susceptible-Infectious-Recovered-Deceased (SIRD) model was used for the analysis of B.1.617 variant in India to estimate its higher infectivity than the wild one. With that in mind, animal contact, social network, technology detection and government deals are raised as important drivers of transmission. Furthermore, the paper also revealed that particular special mutations in B.1.617 variant such as T478K, L452R in the S protein might affect viral fitness [1], making it highly infectious, based on the structural and binding affinity comparison to wild-type and B.1.617 variant with human ACE2. © 2022 ACM.

The susceptible-infected-recovered-dead model for long-term identification of key epidemiological parameters of COVID-19 in Indonesia

The COVID-19 epidemic has spread massively to almost all countries including Indonesia, in just a few months. An important step to overcoming the spread of the COVID-19 is understanding its epidemiology through mathematical modeling intervention. Knowledge of epidemic dynamics patterns is an important part of making timely decisions and preparing hospitals for the outbreak peak. In this study, we developed the susceptible-infected-recovered-dead (SIRD) model, which incorporates the key epidemiological parameters to model and estimate the long-term spread of the COVID-19. The proposed model formulation is data-based analysis using public COVID-19 data from March 2, 2020 to May 15, 2021. Based on numerical analysis, the spread of the pandemic will begin to fade out after November 5, 2021. As a consequence of this virus attack, the cumulative number of infected, recovered, and dead people were estimated at ≈ 3,200,000, ≈ 3,437,000 and ≈ 63,000 people, respectively. Besides, the key epidemiological parameter indicates that the average reproduction number value of COVID-19 in Indonesia is 7.32. The long-term prediction of COVID-19 in Indonesia and its epidemiology can be well described using the SIRD model. The model can be applied in specific regions or cities in understanding the epidemic pattern of COVID-19.

Tracking R-t of COVID-19 Vaccine Effectiveness Using Kalman Filter and SIRD Model

In this paper, a SIRD model is adapted to study the vaccine's impact on the spread of coronavirus (COVID19) spread in Lebanon. To describe the epidemic development across the country, a Kalman filter is integrated with the SIRD model in order to estimate the time-varying reproduction number R-t - is the most important indicator that predicts the severity of an epidemic outbreak. R-t denotes the number of healthy persons to whom an infected person can spread the disease. The results show a reduction in the spread of the pandemic after employing the vaccine. All the data and relevant codebase are available at https://www.moph.gov.lb

A comparative study for predictive monitoring of COVID-19 pandemic.

COVID-19 pandemic caused by novel coronavirus (SARS-CoV-2) crippled the world economy and engendered irreparable damages to the lives and health of millions. To control the spread of the disease, it is important to make appropriate policy decisions at the right time. This can be facilitated by a robust mathematical model that can forecast the prevalence and incidence of COVID-19 with greater accuracy. This study presents an optimized ARIMA model to forecast COVID-19 cases. The proposed method first obtains a trend of the COVID-19 data using a low-pass Gaussian filter and then predicts/forecasts data using the ARIMA model. We benchmarked the optimized ARIMA model for 7-days and 14-days forecasting against five forecasting strategies used recently on the COVID-19 data. These include the auto-regressive integrated moving average (ARIMA) model, susceptible-infected-removed (SIR) model, composite Gaussian growth model, composite Logistic growth model, and dictionary learning-based model. We have considered the daily infected cases, cumulative death cases, and cumulative recovered cases of the COVID-19 data of the ten most affected countries in the world, including India, USA, UK, Russia, Brazil, Germany, France, Italy, Turkey, and Colombia. The proposed algorithm outperforms the existing models on the data of most of the countries considered in this study.

Modelling and projections of the COVID-19 epidemic and the potential impact of social distancing in Cameroon.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19) pandemic continues to be a global health problem with a significant impact in Cameroon. The aim of this study was to improve the understanding of the spread of COVID-19 and enhance disease control strategies. We assessed the SIRD (susceptible, infected, recovered and death) model to describe COVID-19 reported cases in Cameroon from March 7 to May 31, 2020, and study the impact of social distancing. We assessed changes in the basic reproduction number (R0) on a phaseadjusted process and forecasted the longterm epidemic trend. Daily incidence data was fitted to a log-linear model before each peak of the epidemic with the purpose of studying the effective mechanism of variation of the reproduction number Re. Before the first peak of the epidemic, R0 was estimated as 6.8. Social distancing and restricted measures contributed to reduce the value to 3.24 by April 30 but remained greater than 1 (R0=2.43) by May 22 when the initial measures implemented by the government to control the spread of the disease were relaxed. The estimated number of infections ranged 13,703-18,456 by May 31, and will continue increasing throughout June 2020 with more than 20,000 cases expected by the end of June 2020, suggesting that the pandemic is still in the growth phase. Longterm prediction showed a flattened curve towards April 2021. Preventive measures initially implemented by the government of Cameroon should be strictly maintained and reinforced to reduce Re to 0.5.

Reconstructing the Effectiveness of Policy Measures to Avoid Next-Wave COVID-19 Infections and Deaths Using a Dynamic Simulation Model: Implications for Health Technology Assessment.

OBJECTIVE: The goal of this study was to dynamically model next-wave scenarios to observe the impact of different lockdown measures on the infection rates (IR) and mortality for two different prototype countries, mimicking the 1st year of the COVID-19 pandemic in Europe. METHODS: A dynamic simulation SIRD model was designed to assess the effectiveness of policy measures on four next-wave scenarios, each preceded by two different lockdowns. The four scenarios were (1) no-measures, (2) uniform measures, (3) differential measures based on isolating > 60 years of age group, and (4) differential measures with additional contact reduction measures for the 20-60 years of age group. The dynamic simulation model was prepared for two prototype European countries, Northwestern (NW) and Southern (S) country. Both prototype countries were characterized based on age composition and contact matrix. RESULTS: The results show that the outcomes of the next-wave scenarios depend on number of infections of previous lockdowns. All scenarios reduce the incremental deaths compared with a no-measures scenario. Differential measures show lower number of deaths despite an increase of infections. Additionally, prototype S shows overall more deaths compared with prototype NW due to a higher share of older citizens. CONCLUSION: This study shows that differential measures are a worthwhile option for controlling the COVID-19 epidemic. This may also be the case in situations where relevant parts of the population have taken up vaccination. Additionally, the effectiveness of interventions strongly depends on the number of previously infected individuals. The results of this study may be useful when planning and forecasting the impact of non-pharmacological interventions and vaccination campaigns.

On the evolution of the COVID-19 epidemiological parameters using only the series of deceased. A study of the Spanish outbreak using Genetic Algorithms.

We propose a methodology for estimating the evolution of the epidemiological parameters of a SIRD model (acronym of Susceptible, Infected, Recovered and Deceased individuals) which allows to evaluate the sanitary measures taken by the government, for the COVID-19 in the Spanish outbreak. In our methodology the only information required for estimating these parameters is the time series of deceased people; due to the number of asymptomatic people produced by the COVID-19, it is not possible to know the actual number of infected people at any given time. Therefore, among the different time series that quantify the pandemic we consider just the number of deceased people to minimize the square sum of errors. The time series of deaths considered runs from March to the end of September and is divided into four sub-periods reflecting the different isolation measures taken by the Spanish government. The parameters that we can estimate are the time from the beginning of the disease, the transmission rate, and the recovery rate; these last two ratios are estimated in each of the different sub-periods. In this way the model considered has 2x4+1=9 parameters that are estimated jointly over the whole period from the data of deceased. Given the complexity of the model, to estimate the parameters that minimize the square sum of errors, a Genetic Algorithm is used. Our methodology confirms the effectiveness of the sanitary measures taken by the Spanish government showing a dramatic reduction in the basic reproductive number R 0 during confinement; also, a further increase in R 0 after the end of the alarm state decreed by the government on June 21 was detected. Our results also point out that the Patient Zero in the COVID-19 Spanish outbreak emerged between the end of December and early January, at least four weeks before January 31st, that was the moment when the Spanish authorities reported the first positive case.