Susceptible-exposed-infectious-recovered Research Articles

A switching state-space transmission model for tracking epidemics and assessing interventions

The effective control of infectious diseases relies on accurate assessment of the impact of interventions, which is often hindered by the complex dynamics of the spread of disease. A Beta-Dirichlet switching state-space transmission model is proposed to track underlying dynamics of disease and evaluate the effectiveness of interventions simultaneously. As time evolves, the switching mechanism introduced in the susceptible-exposed-infected-recovered (SEIR) model is able to capture the timing and magnitude of changes in the transmission rate due to the effectiveness of control measures. The implementation of this model is based on a particle Markov Chain Monte Carlo algorithm, which can estimate the time evolution of SEIR states, switching states, and high-dimensional parameters efficiently. The efficacy of the proposed model and estimation procedure are demonstrated through simulation studies. With a real-world application to British Columbia's COVID-19 outbreak, the proposed switching state-space transmission model quantifies the reduction of transmission rate following interventions. The proposed model provides a promising tool to inform public health policies aimed at studying the underlying dynamics and evaluating the effectiveness of interventions during the spread of the disease.

Modelling COVID-19 Vaccination in the UK: Impact of the Autumn 2022 and Spring 2023 Booster Campaigns.

The delivery of COVID-19 vaccines was successful in reducing hospitalizations and mortality. However, emergence of the Omicron variant resulted in increased virus transmissibility. Consequently, booster vaccination programs were initiated to decrease the risk of severe disease and death among vulnerable members of the population. This study aimed to estimate the effects of the booster program and alternative vaccination strategies on morbidity and mortality due to COVID-19 in the UK. A Susceptible-Exposed-Infectious-Recovered (SEIR) model was used to assess the impact of several vaccination strategies on severe outcomes associated with COVID-19, including hospitalizations, mortality, National Health Service (NHS) capacity quantified by hospital general ward and intensive care unit (ICU) bed days, and patient productivity. The model accounted for age-, risk- and immunity-based stratification of the UK population. Outcomes were evaluated over a 48-week time horizon from September 2022 to August 2023 considering the actual UK autumn 2022/spring 2023 booster campaigns and six counterfactual strategies. The model estimated that the autumn 2022/spring 2023 booster campaign resulted in a reduction of 18,921hospitalizations and 1463 deaths, compared with a no booster scenario. Utilization of hospital bed days due to COVID-19 decreased after the autumn 2022/spring 2023 booster campaign. Expanding the booster eligibility criteria and improving uptake improved all outcomes, including averting twice as many ICU admissions, preventing more than 20% additional deaths, and a sevenfold reduction in long COVID, compared with the autumn 2022/spring 2023 booster campaign. The number of productive days lost was reduced by fivefold indicating that vaccinating a wider population has a beneficial impact on the morbidities associated with COVID-19. Our modelling demonstrates that the autumn 2022/spring 2023 booster campaign reduced COVID-19-associated morbidity and mortality. Booster campaigns with alternative eligibility criteria warrant consideration in the UK, given their potential to further reduce morbidity and mortality as future variants emerge.

The Potential Economic Impact of the Updated COVID-19 mRNA Fall 2023 Vaccines in Japan.

This analysis estimates the economic and clinical impact of a Moderna updated COVID-19 mRNA Fall 2023 vaccine for adults ≥18 years in Japan. A previously developed Susceptible-Exposed-Infected-Recovered (SEIR) model with a one-year analytic time horizon (September 2023-August 2024) and consequences decision tree were used to estimate symptomatic infections, COVID-19 related hospitalizations, deaths, quality-adjusted life years (QALYs), costs, and incremental cost-effectiveness ratio (ICER) for a Moderna updated Fall 2023 vaccine versus no additional vaccination, and versus a Pfizer-BioNTech updated mRNA Fall 2023 vaccine. The Moderna vaccine is predicted to prevent 7.2 million symptomatic infections, 272,100 hospitalizations and 25,600 COVID-19 related deaths versus no vaccine. In the base case (healthcare perspective), the ICER was ¥1,300,000/QALY gained ($9400 USD/QALY gained). Sensitivity analyses suggest results are most affected by COVID-19 incidence, initial vaccine effectiveness (VE), and VE waning against infection. Assuming the relative VE between both bivalent vaccines apply to updated Fall 2023 vaccines, the base case suggests the Moderna version will prevent an additional 1,100,000 symptomatic infections, 27,100 hospitalizations, and 2600 deaths compared to the Pfizer-BioNTech vaccine. The updated Moderna vaccine is expected to be highly cost-effective at a ¥5 million willingness-to-pay threshold across a wide range of scenarios.

A Review of Environmental Factors for an Ontology-Based Risk Analysis for Pandemic Spread

Contact tracing is a method used to control the spread of a pandemic. The objectives of this research are to conduct an empirical review and content analysis to identify the environmental factors causing the spread of the pandemic and to propose an ontology-based big data architecture to collect these factors for prediction. No research studies these factors as a whole in pandemic prediction. The research method used was an empirical study and content analysis. The keywords contact tracking, pandemic spread, fear, hygiene measures, government policy, prevention programs, pandemic programs, information disclosure, pandemic economics, and COVID-19 were used to archive studies on the pandemic spread from 2019 to 2022 in the EBSCOHost databases (e.g., Medline, ERIC, Library Information Science & Technology, etc.). The results showed that only 84 of the 588 archived studies were relevant. The risk perception of the pandemic (n = 14), hygiene behavior (n = 7), culture (n = 12), and attitudes of government policies on pandemic prevention (n = 25), education programs (n = 2), business restrictions (n = 2), technology infrastructure, and multimedia usage (n = 24) were the major environmental factors influencing public behavior of pandemic prevention. An ontology-based big data architecture is proposed to collect these factors for building the spread prediction model. The new method overcomes the limitation of traditional pandemic prediction model such as Susceptible-Exposed-Infected-Recovered (SEIR) that only uses time series to predict epidemic trend. The big data architecture allows multi-dimension data and modern AI methods to be used to train the contagion scenarios for spread prediction. It helps policymakers to plan pandemic prevention programs.

Simulations for the Susceptible-Exposed-Infected-Recovered (SEIR) Model in the Forecast of Epidemic Outbreak

Mathematical modelling based on the compartmental Susceptible – Exposed – Infected - Recovered (SEIR) model is proposed in this paper to study the pandemic outbreak. In addition, simulations from both the deterministic approach as well as the stochastic approach are implemented to validate the present study. Among each state, the transitions between different categories are simulated by using various graph models including the complete graph, the steady Erdos-Renyi graph, as well as the varying Erdos-Renyi graph. The related parameters for the SEIR model are chosen from available literature and the effect of some other factors such as the inflow or outflow of travellers in a city as well as the impact of vaccination rate is explored. Furthermore, the difference between the simulation results coming from the deterministic SEIR model and the stochastic SEIR one is examined to check the availability of the present simulation. It is found that, the stochastic simulation based on the complete graph is more consistent with the deterministic SEIR model.

Multi-feature SEIR model for epidemic analysis and vaccine prioritization.

The SEIR (susceptible-exposed-infected-recovered) model has become a valuable tool for studying infectious disease dynamics and predicting the spread of diseases, particularly concerning the COVID pandemic. However, existing models often oversimplify population characteristics and fail to account for differences in disease sensitivity and social contact rates that can vary significantly among individuals. To address these limitations, we have developed a new multi-feature SEIR model that considers the heterogeneity of health conditions (disease sensitivity) and social activity levels (contact rates) among populations affected by infectious diseases. Our model has been validated using the data of the confirmed COVID cases in Allegheny County (Pennsylvania, USA) and Hamilton County (Ohio, USA). The results demonstrate that our model outperforms traditional SEIR models regarding predictive accuracy. In addition, we have used our multi-feature SEIR model to propose and evaluate different vaccine prioritization strategies tailored to the characteristics of heterogeneous populations. We have formulated optimization problems to determine effective vaccine distribution strategies. We have designed extensive numerical simulations to compare vaccine distribution strategies in different scenarios. Overall, our multi-feature SEIR model enhances the existing models and provides a more accurate picture of disease dynamics. It can help to inform public health interventions during pandemics/epidemics.

Estimation of the Impact of Vaccination Intervention on Recovered Coronavirus Patients

This work estimated the impact of vaccination intervention on coronavirus patients who have recovered from the disease and the vulnerability index of the recovered population due to the impact of vaccine was also investigated. This work adopted a numerical solution to study the continuous dynamical system of linear first order differential equations describing a SEIR (Susceptible, Exposed, Infected, Recovered) model on the spread of Coronavirus Disease – 2019 (COVID-19). To tackle this problem, MATLAB ordinary differential equation of order 45 (ODE45) numerical method was adopted for the analysis. The vulnerability index of the recovered population was low due to the impact of vaccine meaning that the recovered population will gain immunity and they will not be re-infected. The study recommended that coronavirus patients who have recovered from the disease should ensure that they have vaccination administered to them to avoid re-occurrence of the virus attack as an intervention strategy.

Simulating the impact of optimized prevention and control measures on the transmission of monkeypox in the United States: A model-based study.

This study aimed to develop a modified susceptible-exposed-infected-recovered (SEIR) model to evaluate monkeypox epidemics in the United States and explore more optimized prevention and control measures. To further assess the impact of public health measures on the transmission of monkeypox, different intervention scenarios were developed based on the classic SEIR model, considering reducing contact, enhancing vaccination, diagnosis delay, and environmental transmission risk, respectively. We evaluated the impact of different measures by simulating their spread in different scenarios. During the simulation period, 8709 people were infected with monkeypox. The simulation analysis showed that: (1) the most effective measures to control monkeypox transmission during the early stage of the epidemic were reducing contact and enhancing vaccination, with cumulative infections at 51.20% and 41.90% of baseline levels, respectively; (2) shortening diagnosis time would delay the peak time of the epidemic by 96 days; and (3) the risk of environmental transmission of monkeypox virus was relatively low. This study indirectly proved the effectiveness of the prevention and control measures, such as reducing contact, enhancing vaccination, shortening diagnosis time, and low risk of environmental transmission, which also provided an important reference and containment experience for nonepidemic countries.

Contributing to health system resilience during pandemics via purchasing and supply strategies: an exploratory system dynamics approach

BackgroundHealth systems worldwide struggled to obtain sufficient personal protective equipment (PPE) and ventilators during the COVID-19 pandemic due to global supply chain disruptions. Our study’s aim was to create a proof-of-concept model that would simulate the effects of supply strategies under various scenarios, to ultimately help decision-makers decide on alternative supply strategies for future similar health system related crises.MethodsWe developed a system dynamics model that linked a disease transmission model structure (susceptible, exposed, infectious, recovered (SEIR)) with a model for the availability of critical supplies in hospitals; thereby connecting care demand (patients’ critical care in hospitals), with care supply (available critical equipment and supplies). To inform the model structure, we used data on critical decisions and events taking place surrounding purchase, supply, and availability of PPE and ventilators during the first phase of the COVID-19 pandemic within the English national health system. We used exploratory modelling and analysis to assess the effects of uncertainties on different supply strategies in the English health system under different scenarios. Strategies analysed were: (i) purchasing from the world market or (ii) through direct tender, (iii) stockpiling, (iv) domestic production, (v) supporting innovative supply strategies, or (vi) loaning ventilators from the private sector.ResultsWe found through our exploratory analysis that a long-lasting shortage in PPE and ventilators is likely to be apparent in various scenarios. When considering the worst-case scenario, our proof-of-concept model shows that purchasing PPE and ventilators from the world market or through direct tender have the greatest influence on reducing supply shortages, compared to producing domestically or through supporting innovative supply strategies. However, these supply strategies are affected most by delays in their shipment time or set-up.ConclusionWe demonstrated that using a system dynamics and exploratory modelling approach can be helpful in identifying the purchasing and supply chain strategies that contribute to the preparedness and responsiveness of health systems during crises. Our results suggest that to improve health systems’ resilience during pandemics or similar resource-constrained situations, purchasing and supply chain decision-makers can develop crisis frameworks that propose a plan of action and consequently accelerate and improve procurement processes and other governance processes during health-related crises; implement diverse supplier frameworks; and (re)consider stockpiling. This proof-of-concept model demonstrates the importance of including critical supply chain strategies as part of the preparedness and response activities to contribute to health system resilience.

Impact of policy incentives on technological innovation and diffusion within the new-energy vehicle industry: an ecosystem approach

ABSTRACT In the context of developing emerging industries in China, this study examines the specific effects of policy incentives on new energy vehicle (NEV) technological innovation and constructs a susceptible-exposed-infected-recovered (SEIR) infection model to simulate the diffusion of the NEV technological innovation process. The results highlight four main findings. First, an inverted U-shaped relationship exists between government-supported policies and technological innovation in NEVs. Second, regulatory policies can significantly incentivize the innovative behaviour of NEV enterprises. Third, the effects of policy incentives are more evident in the technological innovation of non-state-owned enterprises than in state-owned enterprises. Fourth, the SEIR infection model shows that policy incentives significantly accelerate the process of utilising, optimising, and replacing technologies in the technology innovation ecosystem, significantly shortening the iterative process of the entire system.

The Predicted Potential Impact of COVID-19 Pandemic on Tuberculosis Epidemic in Tamil Nadu, South India.

To estimate the prevalence and incidence of TB before and during the COVID-19 pandemic in Tamil Nadu, south India. In the present study, the effect of COVID-19 epidemiology on the TB epidemic was assessed by the SEIR (Susceptible-Exposed-Infected-Recovered), a compartmental epidemiological model. The model input parameters on compartments of TB and incidence of COVID-19 were collected from the published literature. Based on the data collected, point prevalence and incidence of TB per 100,000 population is calculated with and without COVID-19. A prediction was conducted up to 2025, trend analysis was performed, and a trend chi-square test and chi-square test of independence were used to test the difference between the prevalence with and without COVID-19. R software 2000 (R 4.0.0) was used for analysis. The TB prevalence without and with COVID-19 decreases from 289 in 2020 to 271 in 2025 and from 289 in 2020 to 269 in 2025, respectively. Similarly, the incidence of TB was decreasing from 144 in 2020 to 135 in 2025 without COVID-19 and 143 in 2020 to 134 in 2025 with COVID-19. Though the TB burden is decreasing over the years, the trend was not statistically significant (p > 0.05). With respect to the district level, the prevalence and incidence of TB with and without COVID-19 is also found to be decreasing over the years. It was also found that the difference in the prevalence and incidence of TB with and without COVID-19 was not statically significant. The results of our study shows that there was an annual decline of around 2% from 2020 to 2025 in the trend of the prevalence and incidence of TB with and without COVID-19. Overall, there is a reduction, but it was not significant, and there is no significant effect of COVID-19 on TB in Tamil Nadu.

Using hybrid automata to model mitigation of global disease spread via travel restriction

<abstract><p>Inspired by the COVID-19 pandemic, we build a large-scale epidemiological model that accounts for coordination between regions, each using travel restrictions in order to attempt to mitigate the spread of disease. There is currently a need for simulations of countries cooperating since travel restriction policies are typically taken without global considerations. It is possible, for instance, that a strategy which appears unfavorable to a region at some point during a pandemic might be best for containing the global spread, or that only by coordinating policies among several regions can a restriction strategy be truly effective. We use the formalism of hybrid automata to model the global disease spread among the coordinating regions. We model a connected network of coupled Susceptible-Exposed-Infected-Recovered (SEIR) models by considering a weighted directed graph with each node corresponding to a single region's disease model. The SEIR dynamics for each region admit terms for inter-regional travel determined by the graph's Laplacian that additionally accounts for travel restrictions between regions. The existence of an edge may change according to so-called guard conditions, which are triggered when the proportion of symptomatic infected individuals in a region reaches a critical value. Lastly, we run simulations in MATLAB of a global disease spreading among regions using automated travel restrictions and analyze the results.</p></abstract>

Analysis of a COVID-19 model with media coverage and limited resources

<abstract><p>The novel coronavirus disease (COVID-19) pandemic has profoundly impacted the global economy and human health. The paper mainly proposed an improved susceptible-exposed-infected-recovered (SEIR) epidemic model with media coverage and limited medical resources to investigate the spread of COVID-19. We proved the positivity and boundedness of the solution. The existence and local asymptotically stability of equilibria were studied and a sufficient criterion was established for backward bifurcation. Further, we applied the proposed model to study the trend of COVID-19 in Shanghai, China, from March to April 2022. The results showed sensitivity analysis, bifurcation, and the effects of critical parameters in the COVID-19 model.</p></abstract>

Exploring of Homotopy Perturbation Method (HPM) for Solving Spread of COVID-19

This article discusses the solution to the non-linear differential equation system for the spread of COVID19 with SEIR (Susceptible, Exposed, Infected, Recovered) model using the Homotopy Perturbation Method. Specifically, this article examines the impact of moving the recovered subpopulation back to the susceptible subpopulation on the spread of COVID-19 in the city of Medan. The data used is real data for the city of Medan in 2021. The results of constructing a model for the spread of COVID-19 were analyzed to obtain a disease-free critical point. By using the Next Generation Matrix method, the Basic Reproduction number R0 = 4.61 is obtained, this indicates that COVID-19 is still possible to spread in Medan City. Simulations using the Homotopy Perturbation Method numerical approach and the results compared with the Runge Kutte Order 4 method show results that accurately describe the dynamics of the spread of COVID-19 in Medan City. The very small error indicates that the Homotopy Perturbation Method is very suitable for use in solving non-linear differential equation systems, especially in the SEIRS model of the spread of COVID-19. The simulation results show that the impact of the movement of recovered sub-populations to susceptible sub-populations results in accelerated transmission of COVID-19. The greater the number of movements higher the rate of spread of COVID-19.

Selection of models and parameter estimation for monitoring the COVID-19 epidemic in Brazil via Bayesian inference

In 2019, a new strain of coronavirus led to an outbreak of disease cases named COVID-19, evolving rapidly into a pandemic. In Brazil, delayed decision making and lack of knowledge have resulted in an alarming increase in daily transmission and deaths. In this context, researchers used mathematical models to assist in determining the parameters that act in the spread of diseases, revealing containment measures. However, numerous mathematical models exist in the literature, each with specific parameters to be specified, leading to an important question about which model best represents the pandemic behavior. In this regard, this work aims to apply the Approximate Bayesian Computation method to select the best model and simultaneously estimate the parameters to resolve the abovementioned issue. The models adopted were susceptible-infected-recovered (SIR), susceptible-exposed-infected-recovered (SEIR), susceptible-infected-recovered-susceptible (SIRS), and susceptible-exposed-infected-recovered-susceptible (SEIRS). Approximate Bayesian Computation Monte Carlo Sequencing (ABC-SMC) was used to select among four competing models to represent the number of infected individuals and to estimate the model parameters based on three periods of Brazil COVID-19 data. A forecasting test was performed to test the ABC-SMC algorithm and the selected models for two months. The result was compared with the actual number of infected that were reported. Among the teste models, it was found that the ABC-SMC algorithm had a promising performance, since the data were noisy and the models could not predict all parameters.

Deep reinforcement learning based medical supplies dispatching model for major infectious diseases: Case study of COVID-19

Stockpiling and scheduling plans for medical supplies represent essential preventive and control measures in major public health events. In the face of major infectious diseases, such as the novel coronavirus disease (COVID-19), the outbreak trend and variability of disease strains are often unpredictable. Hence, it is necessary to optimally adjust the prevention and control dispatching strategy according to the circumstances and outbreak locations to maintain economic development while ensuring the human health survival, however, many models in this scenario seldom consider the dynamic material prediction and the measurement of multiple costs at the same time. Taking the COVID-19 scenario as a case study, we establish a deep reinforcement learning (DRL)-based medical supplies dispatching (MSD) model for major infectious diseases, considering the volatility of the COVID-19 situation and the discrepancy between medical material demand and supply due to the high infectiousness of the Omicron series strains. The present model has three main components: 1) First, for the dynamic medical material prediction problem in complex infectious disease scenarios, taking the lifted COVID-19 lockdown scenario as an example, the modified susceptible-exposed-infected-recovered (SEIR) model was utilized to analyze the spread of the COVID-19, understand its characteristics, and map out the related medical supplies demand; 2) Second, to break away from the previous premise of only considering supply-demand, this study adds scheduling rules and cost function that weighs health and economic costs. An epidemic dispatching optimization model (Epi_DispatchOptim) was established using the OpenAI Gym toolkit to form an environment structure with virus transmission space, and emergency MSD while considering both human health and economic costs. This architecture interprets the balance between the supply-demand of medical supplies and reflects the importance of MSD in the balanced development of health and economy under the spread of infectious diseases; 3) Finally, the MSD strategy under the balance of health and economic cost is explored in Epi_DispatchOptim using reinforcement learning (RL) and the evolutionary algorithm (EA). Experiments conducted on two datasets indicate that the RL and EA reduce economic as well as health costs compared to the original environmental strategies. The above study illustrates how to use epidemiological models to predict the demand for healthcare supplies as the premise of scheduling models, and use Epi_DispatchOptim to explore the dynamic MSD decisions under mortality and economic equilibrium. In Shanghai, China, the economic cost of the exploration strategy is reduced by 27.36–27.07B compared to static scheduling, and deaths are reduced by 126–150 in 150 day compared to the no-intervention scenario. By integrating knowledge of epidemiology, optimal decision making, and economics, Epi_DispatchOptim further constructs epidemiological models, cost functions, state-action spaces, and other modules to assist public health decision makers in adopting appropriate MSD strategies for major public health event.

Envisioning urban environments resilient to vector-borne diseases: a protocol to study dengue in Vietnam

Transmitted primarily by Aedes aegypti (Ae. aegypti) and Aedes albopictus (Ae. albopictus), arboviral diseases pose a major global public health threat. Dengue, chikungunya, and Zika are increasingly prevalent in Southeast Asia. Among other arboviruses, dengue and Zika are becoming more common in Central and South America. Given human encroachment into previously uninhabited, often deforested areas, to provide new housing in regions of population expansion, conceptualizing built urban environments in a novel way is urgently needed to safeguard against the growing climate change-driven threat of vector-borne diseases. By understanding the spread from a One Health perspective, enhanced control and prevention can be achieved. This is particularly important considering that climate change is likely to significantly impact the persistence of ponded water where mosquitoes breed due to increasing temperature and shifting rainfall patterns with regard to magnitude, duration, frequency, and season. Models can incorporate aquatic mosquito stages and adult spatial dynamics when habitats are heterogeneously available, thereby including dispersal and susceptible-exposed-infected-recovered (SEIR) epidemiology. Coupled with human population distribution (density, locations), atmospheric conditions (air temperature, precipitation), and hydrological conditions (soil moisture distribution, ponding persistence in topographic depressions), modeling has improved predictive ability for infection rates. However, it has not informed interventional approaches from an urban environment perspective which considers the role of ponds/lakes that support green spaces, the density of population that enables rapid spread of disease, and varying micro-habitats for various mosquito stages under climate change. Here, for an example of dengue in Vietnam, a preventive and predictive approach to design resilient urban environments is proposed, which uses data from rapidly expanding metropolitan communities to learn continually. This protocol deploys computational approaches including simulation and machine learning/artificial intelligence, underpinned by surveillance and medical data for validation and adaptive learning. Its application may best inform urban planning in low-middle income countries in tropical zones where arboviral pathogens are prevalent.

A Multi-Objective Simulated Annealing Local Search Algorithm in Memetic CENSGA: Application to Vaccination Allocation for Influenza

Flu vaccine allocation is of great importance for safeguarding public health and mitigating the impact of influenza outbreaks. In this regard, decision-makers face multifaceted challenges, including limited vaccine supply, targeting vulnerable people, adapting to regional variations, ensuring fairness in distribution, and promoting public trust. The objective of this work is to address the vaccination allocation problem by introducing a novel optimization scheme with the simulated annealing (SA) algorithm. A dual-objective model is developed to both manage infection rates and minimize the unit cost of the vaccination campaign. The proposed approach is designed to promote convergence toward the best Pareto front in multi-objective optimization, wherein SA attempts to embed diversity and uniformity within a memetic version of the controlled elitism nondominated sorting genetic algorithm (CENSGA). To model the underlying vaccination allocation problem, the dynamics of the disease are described using the susceptible–exposed–infectious–recovered (SEIR) epidemiological model to better express hidden flu characteristics. This model specifically analyzes the effects of pulsive vaccination allocation in two phases aiming to minimize the number of infected individuals to an acceptable level in a finite amount of time, which can help in stabilizing the model against sudden flu endemics over the long run. The computational experiments show that the proposed algorithm effectively explores the extensive search space of the vaccination allocation problem. The results of the suggested framework indicate that the obtained Pareto front best represents complete vaccination campaigns. The findings of this research can help in evidence-based decision making that can optimize flu vaccine distribution, contribute to the prevention of illness and reduction in hospitalizations, and potentially save countless lives.

Topic and Trend Analysis of Weibo Discussions About COVID-19 Medications Before and After China's Exit from the Zero-COVID Policy: Retrospective Infoveillance Study.

After 3 years of its zero-COVID policy, China lifted its stringent pandemic control measures with the announcement of the 10 new measures on December 7, 2022. Existing estimates suggest 90%-97% of the total population was infected during December. This change created a massive demand for COVID-19 medications and treatments, either modern medicines or traditional Chinese medicine (TCM). This study aimed to explore (1) how China's exit from the zero-COVID policy impacted media and the public's attention to COVID-19 medications; (2) how social COVID-19 medication discussions were related to existing model estimates of daily cases during that period; (3) what the diversified themes and topics were and how they changed and developed from November 1 to December 31, 2022; and (4) which topics about COVID-19 medications were focused on by mainstream and self-media accounts during the exit. The answers to these questions could help us better understand the consequences of exit strategies and explore the utilities of Sina Weibo data for future infoveillance studies. Using a scrapper for data retrieval and the structural topic modeling (STM) algorithm for analysis, this study built 3 topic models (all data, before a policy change, and after a policy change) of relevant discussions on the Chinese social media platform Weibo. We compared topic distributions against existing estimates of daily cases and between models before and after the change. We also compared proportions of weibos published by mainstream versus self-media accounts over time on different topics. We found that Weibo discussions shifted sharply from concerns of social risks (case tracking, governmental regulations, etc) to those of personal risks (symptoms, purchases, etc) surrounding COVID-19 infection after the exit from the zero-COVID policy. Weibo topics of "symptom sharing" and "purchase and shortage" of modern medicines correlated more strongly with existing susceptible-exposed-infected-recovered (SEIR) model estimates compared to "TCM formulae" and other topics. During the exit, mainstream accounts showed efforts to specifically engage in topics related to worldwide pandemic control policy comparison and regulations about import and reimbursement of medications. The exit from the zero-COVID policy in China was accompanied by a sudden increase in social media discussions about COVID-19 medications, the demand for which substantially increased after the exit. A large proportion of Weibo discussions were emotional and expressed increased risk concerns over medication shortage, unavailability, and delay in delivery. Topic keywords showed that self-medication was sometimes practiced alone or with unprofessional help from others, while mainstream accounts also tried to provide certain medication instructions. Of the 16 topics identified in all 3 STM models, only "symptom sharing" and "purchase and shortage" showed a considerable correlation with SEIR model estimates of daily cases. Future studies could consider topic exploration before conducting predictive infoveillance analysis, even with narrowly defined search criteria with Weibo data.

To Evaluate The Impact Of Expansionary Fiscal Policy In Curbing The Impact Of Unemployment In The Singaporean Economy During The Covid-19 Pandemic

With the COVID-19 pandemic becoming increasingly prominent in early 2020 in Singapore, the amount of cases were on the rise. As shown in the Susceptible-Exposed-Infected-Recovered (SEIR) distribution chart, the number of people infected follows a hump-like pattern, indicating that it increased first and then fell subsequently. Given that this happened in a very short time frame, it is intriguing how the first wave of the COVID-19 pandemic and its subsequent economic consequences were dealt with incredibly well, only possible through economic policy-making, fiscal policy being primarily significant. Expansionary fiscal policy comprises policies of reduction in tax rate and increase in public expenditure undertaken by the government to increase the Aggregate Demand (AD) in the economy.