Modelling transmission and control of the COVID-19 pandemic in Australia

Designing a model to estimate the burden of COVID-19 in Iran

The novel coronavirus disease 2019 (COVID-19) is the latest evidence of an epidemic disease resulting in an extraordinary number of infections and claimed several lives, along with extensive economic and social consequences. In response to the emergency situation, countries introduced different policies to address the situation, with different levels of efficacy. This paper outlines the protocol for developing a model to analyze the burden of COVID-19 in Iran and the effect of policies on the incidence and cumulative death of the disease. The importance of the model lies in the fact that no study, according to the authors' best knowledge, tried to quantify the impact of the disease on Iran society and the impact of various implemented interventions on disease control. Based on a systematic review of COVID-19 prediction models and expert interviews, we developed a system dynamics model that not only includes an epidemic part but also considers the impact of various policies implemented by the Ministry of Health. The epidemic model estimates the incidence and mortality of COVID-19 in Iran. The model also intends to evaluate the effect of implemented policies on these outcomes. The model reflects the continuum of COVID-19 infection and care in Iran (of which some of its elements are unique) and key activities and decisions in delivering care. The model is calibrated and validated using data published by the Ministry of Health of Iran. Finally, the study aims to provide evidence of the impact of interventions intended to curb COVID-19 in Iran. Insights provided by the model will be necessary for controlling either future waves of the disease or similar future pandemics.

Autonomous and policy-induced behavior change during the COVID-19 pandemic: Towards understanding and modeling the interplay of behavioral adaptation

Changes in human behaviors, such as reductions of physical contacts and the adoption of preventive measures, impact the transmission of infectious diseases considerably. Behavioral adaptations may be the result of individuals aiming to protect themselves or mere responses to public containment measures, or a combination of both. What drives autonomous and policy-induced adaptation, how they are related and change over time is insufficiently understood. Here, we develop a framework for more precise analysis of behavioral adaptation, focusing on confluence, interactions and time variance of autonomous and policy-induced adaptation. We carry out an empirical analysis of Germany during the fall of 2020 and beyond. Subsequently, we discuss how behavioral adaptation processes can be better represented in behavioral-epidemiological models. We find that our framework is useful to understand the interplay of autonomous and policy-induced adaptation as a "moving target". Our empirical analysis suggests that mobility patterns in Germany changed significantly due to both autonomous and policy-induced adaption, with potentially weaker effects over time due to decreasing risk signals, diminishing risk perceptions and an erosion of trust in the government. We find that while a number of simulation and prediction models have made great efforts to represent behavioral adaptation, the interplay of autonomous and policy-induced adaption needs to be better understood to construct convincing counterfactual scenarios for policy analysis. The insights presented here are of interest to modelers and policy makers aiming to understand and account for behaviors during a pandemic response more accurately.

Passive immunotherapies for the next influenza pandemic

Influenzavirus is among the most relevant candidates for a next pandemic. We review here the phylogeny of former influenza pandemics, and discuss candidate lineages. After briefly reviewing the other existing antiviral options, we discuss in detail the evidences supporting the efficacy of passive immunotherapies against influenzavirus, with a focus on convalescent plasma.

The relationship between compartment models and their stochastic counterparts: A comparative study with examples of the COVID-19 epidemic modeling

Deterministic compartment models (CMs) and stochastic models, including stochastic CMs and agent-based models, are widely utilized in epidemic modeling. However, the relationship between CMs and their corresponding stochastic models is not well understood. The present study aimed to address this gap by conducting a comparative study using the susceptible, exposed, infectious, and recovered (SEIR) model and its extended CMs from the coronavirus disease 2019 modeling literature. We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations. Based on this equivalence, we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment. The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics. However, it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs. Additionally, we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents. This model offered a balance between computational efficiency and accuracy. The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling. Furthermore, the results had implications for the development of hybrid models that integrated the strengths of both frameworks. Overall, the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.

Forecast of peak infection and estimate of excess deaths in COVID-19 transmission and prevalence in Taiyuan City, 2022 to 2023

In this paper, with the method of epidemic dynamics, we assess the spread and prevalence of COVID-19 after the policy adjustment of prevention and control measure in December 2022 in Taiyuan City in China, and estimate the excess population deaths caused by COVID-19. Based on the transmission mechanism of COVID-19 among individuals, a dynamic model with heterogeneous contacts is established to describe the change of control measures and the population's social behavior in Taiyuan city. The model is verified and simulated by basing on reported case data from November 8th to December 5th, 2022 in Taiyuan city and the statistical data of the questionnaire survey from December 1st to 23rd, 2022 in Neijiang city. Combining with reported numbers of permanent residents and deaths from 2017 to 2021 in Taiyuan city, we apply the dynamic model to estimate theoretical population of 2022 under the assumption that there is no effect of COVID-19. In addition, we carry out sensitivity analysis to determine the propagation character of the Omicron strain and the effect of the control measures. As a result of the study, it is concluded that after adjusting the epidemic policy on December 6th, 2022, three peaks of infection in Taiyuan are estimated to be from December 22nd to 31st, 2022, from May 10th to June 1st, 2023, and from September 5th to October 13th, 2023, and the corresponding daily peaks of new cases can reach 400 000, 44 000 and 22 000, respectively. By the end of 2022, excess deaths can range from 887 to 4887, and excess mortality rate can range from 3.06% to 14.82%. The threshold of the infectivity of the COVID-19 variant is estimated 0.0353, that is if the strain infectivity is above it, the epidemic cannot be control with the previous normalization measures.

Ethical frameworks should be applied to computational modelling of infectious disease interventions

This perspective is part of an international effort to improve epidemiological models with the goal of reducing the unintended consequences of infectious disease interventions. The scenarios in which models are applied often involve difficult trade-offs that are well recognised in public health ethics. Unless these trade-offs are explicitly accounted for, models risk overlooking contested ethical choices and values, leading to an increased risk of unintended consequences. We argue that such risks could be reduced if modellers were more aware of ethical frameworks and had the capacity to explicitly account for the relevant values in their models. We propose that public health ethics can provide a conceptual foundation for developing this capacity. After reviewing relevant concepts in public health and clinical ethics, we discuss examples from the COVID-19 pandemic to illustrate the current separation between public health ethics and infectious disease modelling. We conclude by describing practical steps to build the capacity for ethically aware modelling. Developing this capacity constitutes a critical step towards ethical practice in computational modelling of public health interventions, which will require collaboration with experts on public health ethics, decision support, behavioural interventions, and social determinants of health, as well as direct consultation with communities and policy makers.

SARS-CoV-2 Delta and Omicron community transmission networks as added value to contact tracing

OBJECTIVES

Calculations of SARS-CoV-2 transmission networks at a population level have been limited. Networks that estimate infections between individuals and whether this results in a mutation, can be a way to evaluate fitness of a mutational clone by how much it expands in number as well as determining the likelihood a transmission results in a new variant.

METHODS

Australian Delta and Omicron SARS-CoV-2 sequences were downloaded from GISAID. Transmission networks of infection between individuals were estimated using a novel mathematical method.

RESULTS

Many of the sequences were identical, with clone sizes following power law distributions driven by negative binomial probability distributions for both the number of infections per individual and the number of mutations per transmission (median 0.74 nucleotide changes for Delta and 0.71 for Omicron). Using these distributions, an agent-based model was able to replicate the observed clonal network structure, providing a basis for more detailed COVID-19 modelling. Possible recombination events, tracked by insertion/deletion (indel) patterns, were identified for each variant in these outbreaks.

CONCLUSIONS

This modelling approach reveals key transmission characteristics of SARS-CoV-2 and may complement traditional contact tracing. This methodology can also be applied to other diseases as genetic sequencing of viruses becomes more commonplace.

COVID-19 transmission in U.S. transit buses: A scenario-based approach with agent-based simulation modeling (ABSM)

The transit bus environment is considered one of the primary sources of transmission of the COVID-19 (SARS-CoV-2) virus. Modeling disease transmission in public buses remains a challenge, especially with uncertainties in passenger boarding, alighting, and onboard movements. Although there are initial findings on the effectiveness of some of the mitigation policies (such as face-covering and ventilation), evidence is scarce on how these policies could affect the onboard transmission risk under a realistic bus setting considering different headways, boarding and alighting patterns, and seating capacity control. This study examines the specific policy regimes that transit agencies implemented during early phases of the COVID-19 pandemic in USA, in which it brings crucial insights on combating current and future epidemics. We use an agent-based simulation model (ABSM) based on standard design characteristics for urban buses in USA and two different service frequency settings (10-min and 20-min headways). We find that wearing face-coverings (surgical masks) significantly reduces onboard transmission rates, from no mitigation rates of 85% in higher-frequency buses and 75% in lower-frequency buses to 12.5%. The most effective prevention outcome is the combination of KN-95 masks, open window policies, and half-capacity seating control during higher-frequency bus services, with an outcome of nearly 0% onboard infection rate. Our results advance understanding of COVID-19 risks in the urban bus environment and contribute to effective mitigation policy design, which is crucial to ensuring passenger safety. The findings of this study provide important policy implications for operational adjustment and safety protocols as transit agencies seek to plan for future emergencies.

The application of simulation methods during the COVID-19 pandemic: A scoping review

With the outbreak of COVID-19 pandemic, simulation modelling approaches have become effective tools to simulate the potential effects of different intervention measures and predict the dynamic COVID-19 trends. In this scoping review, Studies published between February 2020 and May 2022 that investigated the spread of COVID-19 using four common simulation modeling methods were systematically reported and summarized. Publication trend, characteristics, software, and code availability of included articles were analyzed. Among the included 340 studies, most articles used agent-based model (ABM; n = 258; 75.9 %), followed by the models of system dynamics (n = 42; 12.4 %), discrete event simulation (n = 25; 7.4 %), and hybrid simulation (n = 15; 4.4 %). Furthermore, our review emphasized the purposes and sample time period of included articles. We classified the purpose of the 340 included studies into five categories, most studies mainly analyzed the spread of COVID-19 under policy interventions. For the sample time period analysis, most included studies analyzed the COVID-19 spread in the second wave. Our findings play a crucial role for policymakers to make evidence-based decisions in preventing the spread of COVID-19 pandemic and help in providing scientific decision-makings resilient to similar events and infectious diseases in the future.

Predictions from standard epidemiological models of consequences of segregating and isolating vulnerable people into care facilities

OBJECTIVES

Since the declaration of the COVID-19 pandemic, many governments have imposed policies to reduce contacts between people who are presumed to be particularly vulnerable to dying from respiratory illnesses and the rest of the population. These policies typically address vulnerable individuals concentrated in centralized care facilities and entail limiting social contacts with visitors, staff members, and other care home residents. We use a standard epidemiological model to investigate the impact of such circumstances on the predicted infectious disease attack rates, for interacting robust and vulnerable populations.

METHODS

We implement a general susceptible-infectious-recovered (SIR) compartmental model with two populations: robust and vulnerable. The key model parameters are the per-individual frequencies of within-group (robust-robust and vulnerable-vulnerable) and between-group (robust-vulnerable and vulnerable-robust) infectious-susceptible contacts and the recovery times of individuals in the two groups, which can be significantly longer for vulnerable people.

RESULTS

Across a large range of possible model parameters including degrees of segregation versus intermingling of vulnerable and robust individuals, we find that concentrating the most vulnerable into centralized care facilities virtually always increases the infectious disease attack rate in the vulnerable group, without significant benefit to the robust group.

CONCLUSIONS

Isolated care homes of vulnerable residents are predicted to be the worst possible mixing circumstances for reducing harm in epidemic or pandemic conditions.

Measuring unequal distribution of pandemic severity across census years, variants of concern and interventions

BACKGROUND

The COVID-19 pandemic stressed public health systems worldwide due to emergence of several highly transmissible variants of concern. Diverse and complex intervention policies deployed over the last years have shown varied effectiveness in controlling the pandemic. However, a systematic analysis and modelling of the combined effects of different viral lineages and complex intervention policies remains a challenge due to the lack of suitable measures of pandemic inequality and nonlinear effects.

METHODS

Using large-scale agent-based modelling and a high-resolution computational simulation matching census-based demographics of Australia, we carried out a systematic comparative analysis of several COVID-19 pandemic scenarios. The scenarios covered two most recent Australian census years (2016 and 2021), three variants of concern (ancestral, Delta and Omicron), and five representative intervention policies. We introduced pandemic Lorenz curves measuring an unequal distribution of the pandemic severity across local areas. We also quantified pandemic biomodality, distinguishing between urban and regional waves, and measured bifurcations in the effectiveness of interventions.

RESULTS

We quantified nonlinear effects of population heterogeneity on the pandemic severity, highlighting that (i) the population growth amplifies pandemic peaks, (ii) the changes in population size amplify the peak incidence more than the changes in density, and (iii) the pandemic severity is distributed unequally across local areas. We also examined and delineated the effects of urbanisation on the incidence bimodality, distinguishing between urban and regional pandemic waves. Finally, we quantified and examined the impact of school closures, complemented by partial interventions, and identified the conditions when inclusion of school closures may decisively control the transmission.

CONCLUSIONS

Public health response to long-lasting pandemics must be frequently reviewed and adapted to demographic changes. To control recurrent waves, mass-vaccination rollouts need to be complemented by partial NPIs. Healthcare and vaccination resources need to be prioritised towards the localities and regions with high population growth and/or high density.

Prediction of Alzheimer's in People with Coronavirus Using Machine Learning

Background

One of the negative effects of the COVID-19 illness, which has affected people all across the world, is Alzheimer's disease. Oblivion after COVID-19 has created a variety of issues for many people. Predicting this issue in COVID-19 patients can considerably lessen the severity of the problem.

Methods

Alzheimer's disease was predicted in Iranian persons with COVID-19 in using three algorithms: Nave Bayes, Random Forest, and KNN. Data collected by private questioner from hospitals of Tehran Province, Iran, during Oct 2020 to Sep 2021. For ML models, performance is quantified using measures such as Precision, Recall, Accuracy, and F1-score.

Results

The Nave Bayes, Random Forest algorithm has a prediction accuracy of higher than 80%. The predicted accuracy of the random forest algorithm was higher than the other two algorithms.

Conclusion

The Random Forest algorithm outperformed the other two algorithms in predicting Alzheimer's disease in persons using COVID-19. The findings of this study could help persons with COVID-19 avoid Alzheimer's problems.

Mass behaviour change amid COVID-19: How public health information and social norms explain the transformation

COVID-19 is a severe and ongoing threat globally, with the spread disrupting lives and society. Despite the developments of vaccines, the key measure to reduce the transmission of variants has stemmed from mass changes to personal behaviours. COVID-19 pandemic offers a unique context, where the protection behaviours enacted by an individual are necessary to keep the community safe. A social psychological perspective can be used to understand the reasons for adherence to policies and determine what other factors can shape preventive behaviours. To resolve this, in partnership with health consumers we use an online survey, with the findings substantiating preventive behaviours are positively related to COVID-19 information access and descriptive norms. Additionally, findings demonstrate the mediating role of injunctive norms on preventive behaviour suggesting that policy makers can influence decision-making by promoting health information that provides guidance on acceptable behaviours, but also demonstrates subsequent success. The integrity of the model is substantiated by partial least squares (PLS) testing.

Facility design and worker justice: COVID-19 transmission in meatpacking plants

BACKGROUND

Meatpacking plants were major sources of COVID-19 outbreaks, posing unprecedented risks to employees, family members, and local communities. The effect on food availability during outbreaks was immediate and staggering: within 2 months, the price of beef increased by almost 7% with documented evidence of significant meat shortages. Meatpacking plant designs, in general, optimize on production; this design approach constrains the ability to enhance worker respiratory protection without reducing output.

METHODS

Using agent-based modeling, we simulate the spread of COVID-19 within a typical meatpacking plant design under varying levels of mitigation measures, including combinations of social distancing and masking interventions.

RESULTS

Simulations show an average infection rate of close to 99% with no mitigation, 99% with the policies that US companies ultimately adopted, 81% infected with the combination of surgical masks and distancing policies, and 71% infected with N95 masks and distancing. Estimated infection rates were high, reflecting the duration and exertion of the processing activities and lack of fresh airflow in an enclosed space.

CONCLUSION

Our results are consistent with anecdotal findings in a recent congressional report, and are much higher than US industry has reported. Our results suggest current processing plant designs made rapid transmission of the virus during the pandemic's early days almost inevitable, and implemented worker protections during COVID-19 did not significantly affect the spread of the virus. We argue current federal policies and regulations are insufficient to ensure the health and safety of workers, creating a justice issue, and jeopardizing food availability in a future pandemic.

A data-driven Markov process for infectious disease transmission

The 2019 coronavirus pandemic exudes public health and socio-economic burden globally, raising an unprecedented concern for infectious diseases. Thus, describing the infectious disease transmission process to design effective intervention measures and restrict its spread is a critical scientific issue. We propose a level-dependent Markov model with infinite state space to characterize viral disorders like COVID-19. The levels and states in this model represent the stages of outbreak development and the possible number of infectious disease patients. The transfer of states between levels reflects the explosive transmission process of infectious disease. A simulation method with heterogeneous infection is proposed to solve the model rapidly. After that, simulation experiments were conducted using MATLAB according to the reported data on COVID-19 published by Johns Hopkins. Comparing the simulation results with the actual situation shows that our proposed model can well capture the transmission dynamics of infectious diseases with and without imposed interventions and evaluate the effectiveness of intervention strategies. Further, the influence of model parameters on transmission dynamics is analyzed, which helps to develop reasonable intervention strategies. The proposed approach extends the theoretical study of mathematical modeling of infectious diseases and contributes to developing models that can describe an infinite number of infected persons.

Impacts on Human Movement in Australian Cities Related to the COVID-19 Pandemic

No studies have yet examined high-resolution shifts in the spatial patterns of human movement in Australia throughout 2020 and 2021, a period coincident with the repeated enactment and removal of varied governmental restrictions aimed at reducing community transmission of SARS-CoV-2. We compared overlapping timeseries of COVID-19 pandemic-related restrictions, epidemiological data on cases and vaccination rates, and high-resolution human movement data to characterize population-level responses to the pandemic in Australian cities. We found that restrictions on human movement and/or mandatory business closures reduced the average population-level weekly movement volumes in cities, as measured by aggregated travel time, by almost half. Of the movements that continued to occur, long movements reduced more dramatically than short movements, likely indicating that people stayed closer to home. We also found that the repeated lockdowns did not reduce their impact on human movement, but the effect of the restrictions on human movement waned as the duration of restrictions increased. Lastly, we found that after restrictions ceased, the subsequent surge in SARS-CoV-2 transmission coincided with a substantial, non-mandated drop in human movement volume. These findings have implications for public health policy makers when faced with anticipating responses to restrictions during future emergency situations.

Investigation of airborne spread of COVID-19 using a hybrid agent-based model: a case study of the UK

Agent-based models have been proven to be quite useful in understanding and predicting the SARS-CoV-2 virus-originated COVID-19 infection. Person-to-person contact was considered as the main mechanism of viral transmission in these models. However, recent understanding has confirmed that airborne transmission is the main route to infection spread of COVID-19. We have developed a computationally efficient agent-based hybrid model to study the aerial propagation of the virus and subsequent spread of infection. We considered virus, a continuous variable, spreads diffusively in air and members of populations as discrete agents possessing one of the eight different states at a particular time. The transition from one state to another is probabilistic and age linked. Recognizing that population movement is a key aspect of infection spread, the model allows unbiased movement of agents. We benchmarked the model to recapture the temporal stochastic infection count data of the UK. The model investigates various key factors such as movement, infection susceptibility, new variants, recovery rate and duration, incubation period and vaccination on the infection propagation over time. Furthermore, the model was applied to capture the infection spread in Italy and France.

Quantifying the spatial spillover effects of non-pharmaceutical interventions on pandemic risk

BACKGROUND

Non-pharmaceutical interventions (NPIs) implemented in one place can affect neighboring regions by influencing people's behavior. However, existing epidemic models for NPIs evaluation rarely consider such spatial spillover effects, which may lead to a biased assessment of policy effects.

METHODS

Using the US state-level mobility and policy data from January 6 to August 2, 2020, we develop a quantitative framework that includes both a panel spatial econometric model and an S-SEIR (Spillover-Susceptible-Exposed-Infected-Recovered) model to quantify the spatial spillover effects of NPIs on human mobility and COVID-19 transmission.

RESULTS

The spatial spillover effects of NPIs explain [Formula: see text] [[Formula: see text] credible interval: 52.8-[Formula: see text]] of national cumulative confirmed cases, suggesting that the presence of the spillover effect significantly enhances the NPI influence. Simulations based on the S-SEIR model further show that increasing interventions in only a few states with larger intrastate human mobility intensity significantly reduce the cases nationwide. These region-based interventions also can carry over to interstate lockdowns.

CONCLUSIONS

Our study provides a framework for evaluating and comparing the effectiveness of different intervention strategies conditional on NPI spillovers, and calls for collaboration from different regions.

Long-term consequences of COVID-19 on mental health and the impact of a physically active lifestyle: a narrative review

BACKGROUND

Coronavirus-19 disease (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Respiratory viruses damage not only the upper respiratory tract in humans, but also several different organs such as the brain. Some of the neurological consequences of COVID-19 reported are anosmia, headache, stroke, declined cognitive function, and impaired mental health, among others. People who had COVID-19 have a higher risk of sequelae in the central nervous system (CNS). However, it is not known which are all possible sequelae and how long will last the long-term effects of the COVID-19 pandemic on behavioral patterns and quality of life.

AIM

We intend to address the long-term impacts of COVID-19 on mental health and the relevance of physical exercise during the pandemic.

METHODS

We conducted a literature search using PubMed to find the articles that were related to these themes.

RESULTS

We found 23,489 papers initially, and then we applied the inclusion/exclusion criteria to narrow down our search to 3617 articles and selected 1380 eligible articles after a thorough reading of titles and abstracts. The findings indicated that COVID-19 impacted general mental health and led many not only hospitalized patients to develop cognitive decline, memory impairment, anxiety, sleep alterations, and depressive-like behavior. Furthermore, the fear of vaccines and their effects had negatively affected mental health and directly impacted mortality rates in unvaccinated COVID-19 patients.

CONCLUSIONS

Preventive measures must be undertaken, such as the vaccination of the entire population, vaccination hesitancy discouragement by creating awareness among individuals, and people's engagement in a physically active lifestyle, since being physically active is a low-cost and effective measure to restore or inhibit the negative outcomes from COVID-19 on mental health.

City-scale model for COVID-19 epidemiology with mobility and social activities represented by a set of hidden Markov models

BACKGROUND AND OBJECTIVE

SARS-CoV-2 emerged by the end of 2019 and became a global pandemic due to its rapid spread. Various outbreaks of the disease in different parts of the world have been studied, and epidemiological analyses of these outbreaks have been useful for developing models with the aim of tracking and predicting the spread of epidemics. In this paper, an agent-based model that predicts the local daily evolution of the number of people hospitalized in intensive care due to COVID-19 is presented.

METHODS

An agent-based model has been developed, taking into consideration the most relevant characteristics of the geography and climate of a mid-size city, its population and pathology statistics, and its social customs and mobility, including the state of public transportation. In addition to these inputs, the different phases of isolation and social distancing are also taken into account. By means of a set of hidden Markov models, the system captures and reproduces virus transmission associated with the stochastic nature of people's mobility and activities in the city. The spread of the virus in the host is also simulated by following the stages of the disease and by considering the existence of comorbidities and the proportion of asymptomatic carriers.

RESULTS

As a case study, the model was applied to Paraná city (Entre Ríos, Argentina) in the second half of 2020. The model adequately predicts the daily evolution of people hospitalized in intensive care due to COVID-19. This adequacy is reflected by the fact that the prediction of the model (including its dispersion), as with the data reported in the field, never exceeded 90% of the capacity of beds installed in the city. In addition, other epidemiological variables of interest, with discrimination by age range, were also adequately reproduced, such as the number of deaths, reported cases, and asymptomatic individuals.

CONCLUSIONS

The model can be used to predict the most likely evolution of the number of cases and hospital bed occupancy in the short term. By adjusting the model to match the data on hospitalizations in intensive care units and deaths due to COVID-19, it is possible to analyze the impact of isolation and social distancing measures on the disease spread dynamics. In addition, it allows for simulating combinations of characteristics that would lead to a potential collapse in the health system due to lack of infrastructure as well as predicting the impact of social events or increases in people's mobility.

Big data technology in infectious diseases modeling, simulation, and prediction after the COVID-19 outbreak

After the outbreak of COVID-19, the interaction of infectious disease systems and social systems has challenged traditional infectious disease modeling methods. Starting from the research purpose and data, researchers improved the structure and data of the compartment model or used agents and artificial intelligence based models to solve epidemiological problems. In terms of modeling methods, the researchers use compartment subdivision, dynamic parameters, agent-based model methods, and artificial intelligence related methods. In terms of factors studied, the researchers studied 6 categories: human mobility, nonpharmaceutical interventions (NPIs), ages, medical resources, human response, and vaccine. The researchers completed the study of factors through modeling methods to quantitatively analyze the impact of social systems and put forward their suggestions for the future transmission status of infectious diseases and prevention and control strategies. This review started with a research structure of research purpose, factor, data, model, and conclusion. Focusing on the post-COVID-19 infectious disease prediction simulation research, this study summarized various improvement methods and analyzes matching improvements for various specific research purposes.

A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility

The COVID-19 pandemic has created an urgent need for mathematical models that can project epidemic trends and evaluate the effectiveness of mitigation strategies. A major challenge in forecasting the transmission of COVID-19 is the accurate assessment of the multiscale human mobility and how it impacts infection through close contacts. By combining the stochastic agent-based modeling strategy and hierarchical structures of spatial containers corresponding to the notion of geographical places, this study proposes a novel model, Mob-Cov, to study the impact of human traveling behavior and individual health conditions on the disease outbreak and the probability of zero-COVID in the population. Specifically, individuals perform power law-type local movements within a container and global transport between different-level containers. It is revealed that frequent long-distance movements inside a small-level container (e.g., a road or a county) and a small population size reduce both the local crowdedness and disease transmission. It takes only half of the time to induce global disease outbreaks when the population increases from 150 to 500 (normalized unit). When the exponent c 1 of the long-tail distribution of distance k moved in the same-level container, p ( k ) ∼ k - c 1 · level , increases, the outbreak time decreases rapidly from 75 to 25 (normalized unit). In contrast, travel between large-level containers (e.g., cities and nations) facilitates global spread of the disease and outbreak. When the mean traveling distance across containers 1 d increases from 0.5 to 1 (normalized unit), the outbreak occurs almost twice as fast. Moreover, dynamic infection and recovery in the population are able to drive the bifurcation of the system to a "zero-COVID" state or to a "live with COVID" state, depending on the mobility patterns, population number and health conditions. Reducing population size and restricting global travel help achieve zero-COVID-19. Specifically, when c 1 is smaller than 0.2, the ratio of people with low levels of mobility is larger than 80% and the population size is smaller than 400, zero-COVID can be achieved within fewer than 1000 time steps. In summary, the Mob-Cov model considers more realistic human mobility at a wide range of spatial scales, and has been designed with equal emphasis on performance, low simulation cost, accuracy, ease of use and flexibility. It is a useful tool for researchers and politicians to apply when investigating pandemic dynamics and when planning actions against disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11071-023-08489-5.

Global analysis and prediction scenario of infectious outbreaks by recurrent dynamic model and machine learning models: A case study on COVID-19

It is essential to evaluate patient outcomes at an early stage when dealing with a pandemic to provide optimal clinical care and resource management. Many methods have been proposed to provide a roadmap against different pandemics, including the recent pandemic disease COVID-19. Due to recurrent epidemic waves of COVID-19, which have been observed in many countries, mathematical modeling and forecasting of COVID-19 are still necessary as long as the world continues to battle against the pandemic. Modeling may aid in determining which interventions to try or predict future growth patterns. In this article, we design a combined approach for analyzing any pandemic in two separate parts. In the first part of the paper, we develop a recurrent SEIRS compartmental model to predict recurrent outbreak patterns of diseases. Due to its time-varying parameters, our model is able to reflect the dynamics of infectious diseases, and to measure the effectiveness of the restrictive measures. We discuss the stable solutions of the corresponding autonomous system with frozen parameters. We focus on the regime shifts and tipping points; then we investigate tipping phenomena due to parameter drifts in our time-varying parameters model that exhibits a bifurcation in the frozen-in case. Furthermore, we propose an optimal numerical design for estimating the system’s parameters. In the second part, we introduce machine learning models to strengthen the methodology of our paper in data analysis, particularly for prediction scenarios. We use MLP, RBF, LSTM, ANFIS, and GRNN for training and evaluation of COVID-19. Then, we compare the results with the recurrent dynamical system in the fitting process and prediction scenario. We also confirm results by implementing our methods on the released data on COVID-19 by WHO for Italy, Germany, Iran, and South Africa between 1/22/2020 and 7/24/2021, when people were engaged with different variants including Alpha, Beta, Gamma, and Delta. The results of this article show that the dynamic model is adequate for long-term analysis and data fitting, as well as obtaining parameters affecting the epidemic. However, it is ineffective in providing a long-term forecast. In contrast machine learning methods effectively provide disease prediction, although they do not provide analysis such as dynamic models. Finally, some metrics, including RMSE, R-Squared, and accuracy, are used to evaluate the machine learning models. These metrics confirm that ANFIS and RBF perform better than other methods in training and testing zones.

Facilitators and barriers of preventive behaviors against COVID-19 during Ramadan: A phenomenology of Indonesian adults

Introduction

Intercity mobility restriction, physical distancing, and mask-wearing are preventive behaviors to reduce the transmission of COVID-19. However, strong cultural and religious traditions become particular challenges in Indonesia. This study uses the Behavior Change Wheel to explore barriers and facilitators for intercity mobility restriction, physical distancing, and mask-wearing during Ramadan.

Methods

Semi-structured in-depth interviews with 50 Indonesian adults were conducted between 10 April and 4 June 2020. Having mapped codes into the Capacity, Opportunity, Motivation - Behavior (COM-B), and Theoretical Domain Framework (TDF) model, we conducted summative content analysis to analyze the most identified factors to preventive behaviors and proposed interventions to address those factors.

Results

Belief about the consequence of preventive behaviors was the most mentioned facilitator to all preventive behaviors among compliers. However, optimism as a TDF factor was commonly mentioned as a barrier to preventive behaviors among non-compliers, while environmental context and resources were the most commonly mentioned factors for intercity mobility restriction.

Conclusions

Public health intervention should be implemented considering the persuasion and involvement of religious and local leaders. Concerning job and economic context, policy related to the intercity mobility restriction should be reconsidered to prevent a counterproductive effect.

Challenges of carbon emission reduction by the workshop education pattern

The COVID-19 pandemic has forced many conferences and educational events to shift from in-person to online, significantly reducing the carbon footprint associated with these activities. Workshops are a common pattern of thematic learning at the university level, usually involving a series of activities, such as gathering, learning, and dining, for participants from different regions. However, unlike a three-day conference, workshops usually last for seven days or more, resulting in a non-negligible carbon footprint. To resolve this challenge, we have developed a model that provides recommendations for minimizing the carbon footprint of workshops. Using data from the DigitalFUTURES International Workshop on architecture education at Tongji University in China, we calculated the carbon footprint of scenarios with varying workshop durations, participation modes, and transportation methods. Our results show that online workshops can reduce the carbon footprint by up to 88% compared to in-person workshops. Hybrid workshops, which combine online and in-person participation, can also lead to significant carbon reductions, with a 46% online participation rate resulting in an 82% reduction in carbon footprint. However, we recommend that in-person participation be maintained to ensure efficient learning and effective communication. Our work provides a sustainable solution for organizing future workshops with a reduced carbon footprint.

Designing Digital COVID-19 Screening: Insights and Deliberations

Due to the global COVID-19 pandemic, public health control and screening measures have been introduced at healthcare facilities, including those housing our most vulnerable populations. These warning measures situated at hospital entrances are presently labour-intensive, requiring additional staff to conduct manual temperature checks and risk-assessment questionnaires of every individual entering the premises. To make this process more efficient, we present eGate, a digital COVID-19 health-screening smart Internet of Things system deployed at multiple entry points around a children's hospital. This paper reports on design insights based on the experiences of concierge screening staff stationed alongside the eGate system. Our work contributes towards social-technical deliberations on how to improve design and deploy of digital health-screening systems in hospitals. It specifically outlines a series of design recommendations for future health screening interventions, key considerations relevant to digital screening control systems and their implementation, and the plausible effects on the staff who work alongside them.

Parallel evolution and control method for predicting the effectiveness of non-pharmaceutical interventions in pandemics

Aim

Nonpharmaceutical interventions (NPIs) are important strategies to utilize in reducing the negative systemic impact pandemic disasters have on human health. However, early on in the pandemic, the lack of prior knowledge and the rapidly changing nature of pandemics make it challenging to construct effective epidemiological models that can be used for anti-contagion decision-making.

Subject and methods

Based on the parallel control and management theory (PCM) and epidemiological models, we developed a Parallel Evolution and Control Framework for Epidemics (PECFE), which can optimize epidemiological models according to the dynamic information during the evolution of pandemics.

Results

The cross-application between PCM and epidemiological models enabled us to successfully construct an anti-contagion decision-making model for the early stages of COVID-19 in Wuhan, China. Using the model, we estimated the effects of gathering bans, intra-city traffic blockades, emergency hospitals, and disinfection, forecasted pandemic trends under different NPIs strategies, and analyzed specific strategies to prevent pandemic rebounds.

Conclusion

The successful simulation and forecasting of the pandemic showed that the PECFE could be effective in constructing decision models during pandemic outbreaks, which is crucial for emergency management where every second counts.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10389-023-01843-2.

A hybrid compartmental model with a case study of COVID-19 in Great Britain and Israel

Given the severe impact of COVID-19 on several societal levels, it is of crucial importance to model the impact of restriction measures on the pandemic evolution, so that governments are able to make informed decisions. Even though there have been countless attempts to propose diverse models since the rise of the outbreak, the increase in data availability and start of vaccination campaigns calls for updated models and studies. Furthermore, most of the works are focused on a very particular place or application and we strive to attain a more general model, resorting to data from different countries. In particular, we compare Great Britain and Israel, two highly different scenarios in terms of vaccination plans and social structure. We build a network-based model, complex enough to model different scenarios of government-mandated restrictions, but generic enough to be applied to any population. To ease the computational load we propose a decomposition strategy for our model.

A Review of the Machine Learning Algorithms for Covid-19 Case Analysis

The purpose of this article is to see how machine learning (ML) algorithms and applications are used in the COVID-19 inquiry and for other purposes. The available traditional methods for COVID-19 international epidemic prediction, researchers and authorities have given more attention to simple statistical and epidemiological methodologies. The inadequacy and absence of medical testing for diagnosing and identifying a solution is one of the key challenges in preventing the spread of COVID-19. A few statistical-based improvements are being strengthened to answer this challenge, resulting in a partial resolution up to a certain level. ML have advocated a wide range of intelligence-based approaches, frameworks, and equipment to cope with the issues of the medical industry. The application of inventive structure, such as ML and other in handling COVID-19 relevant outbreak difficulties, has been investigated in this article. The major goal of this article is to 1) Examining the impact of the data type and data nature, as well as obstacles in data processing for COVID-19. 2) Better grasp the importance of intelligent approaches like ML for the COVID-19 pandemic. 3) The development of improved ML algorithms and types of ML for COVID-19 prognosis. 4) Examining the effectiveness and influence of various strategies in COVID-19 pandemic. 5) To target on certain potential issues in COVID-19 diagnosis in order to motivate academics to innovate and expand their knowledge and research into additional COVID-19-affected industries.

Agent-based simulation of pedestrian dynamics for exposure time estimation in epidemic risk assessment

Purpose

With the coronavirus disease 2019 (COVID-19) pandemic spreading across the world, protective measures for containing the virus are essential, especially as long as no vaccine or effective treatment is available. One important measure is the so-called physical distancing or social distancing.

Methods

In this paper, we propose an agent-based numerical simulation of pedestrian dynamics in order to assess the behavior of pedestrians in public places in the context of contact transmission of infectious diseases like COVID-19, and to gather insights about exposure times and the overall effectiveness of distancing measures.

Results

To abide by the minimum distance of 1.5 m stipulated by the German government at an infection rate of 2%, our simulation results suggest that a density of one person per 16m² or below is sufficient.

Conclusions

The results of this study give insight into how physical distancing as a protective measure can be carried out more efficiently to help reduce the spread of COVID-19.

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.

Key lessons from the COVID-19 public health response in Australia

Australia avoided the worst effects of the COVID-19 pandemic, but still experienced many negative impacts. Reflecting on lessons from Australia's public health response, an Australian expert panel composed of relevant discipline experts identified the following key lessons: 1) movement restrictions were effective, but their implementation requires careful consideration of adverse impacts, 2) disease modelling was valuable, but its limitations should be acknowledged, 3) the absence of timely national data requires re-assessment of national surveillance structures, 4) the utility of advanced pathogen genomics and novel vaccine technology was clearly demonstrated, 5) decision-making that is evidence informed and consultative is essential to maintain trust, 6) major system weaknesses in the residential aged-care sector require fixing, 7) adequate infection prevention and control frameworks are critically important, 8) the interests and needs of young people should not be compromised, 9) epidemics should be recognised as a 'standing threat', 10) regional and global solidarity is important. It should be acknowledged that we were unable to capture all relevant nuances and context specific differences. However, the intent of this review of Australia's public health response is to critically reflect on key lessons learnt and to encourage constructive national discussion in countries across the Western Pacific Region.

A stochastic inventory model of COVID-19 and robust, real-time identification of carriers at large and infection rate via asymptotic laws

A critical operations management problem in the ongoing COVID-19 pandemic is cognizance of (a) the number of all carriers at large (CaL) conveying the SARS-CoV-2, including asymptomatic ones and (b) the infection rate (IR). Both are random and unobservable, affecting the spread of the disease, patient arrivals to health care units (HCUs) and the number of deaths. A novel, inventory perspective of COVID-19 is proposed, with random inflow, random losses and retrials (recurrent cases) and delayed/distributed exit, with randomly varying fractions of the exit distribution. A minimal construal, it enables representation of COVID-19 evolution with close fit of national incidence profiles, including single and multiple pattern outbreaks, oscillatory, periodic or non-periodic evolution, followed by retraction, leveling off, or strong resurgence. Furthermore, based on asymptotic laws, the minimum number of variables that must be monitored for identifying CaL and IR is determined and a real-time identification method is presented. The method is data-driven, utilizing the entry rate to HCUs and scaled, or dimensionless variables, including the mean residence time of symptomatic carriers in CaL and the mean residence time in CaL of patients entering HCUs. As manifested by several robust case studies of national COVID-19 incidence profiles, it provides efficient identification in real-time under unbiased monitoring error, without relying on any model. The propagation factor, a stochastic process, is reconstructed from the identified trajectories of CaL and IR, enabling evaluation of control measures. The results are useful towards the design of policies restricting COVID-19 and encumbrance to HCUs and mitigating economic contraction.

Persistence of the Omicron variant of SARS-CoV-2 in Australia: The impact of fluctuating social distancing

We modelled emergence and spread of the Omicron variant of SARS-CoV-2 in Australia between December 2021 and June 2022. This pandemic stage exhibited a diverse epidemiological profile with emergence of co-circulating sub-lineages of Omicron, further complicated by differences in social distancing behaviour which varied over time. Our study delineated distinct phases of the Omicron-associated pandemic stage, and retrospectively quantified the adoption of social distancing measures, fluctuating over different time periods in response to the observable incidence dynamics. We also modelled the corresponding disease burden, in terms of hospitalisations, intensive care unit occupancy, and mortality. Supported by good agreement between simulated and actual health data, our study revealed that the nonlinear dynamics observed in the daily incidence and disease burden were determined not only by introduction of sub-lineages of Omicron, but also by the fluctuating adoption of social distancing measures. Our high-resolution model can be used in design and evaluation of public health interventions during future crises.

Numerical simulations on scale-free and random networks for the spread of COVID-19 in Pakistan☆

Epidemiology is the study of how and why an infectious disease occurs in a group of people. Several epidemiological models have been developed to get information on the spread of a disease in society. That information is used to plan strategies to prevent illness and manage patients. But, most of these models consider only random diffusion of the disease and hence ignore the number of interactions among people. To take into account the interactions among individuals, the network approach is becoming increasingly popular. It is novel to consider the dynamics of infectious disease using various networks rather than classical differential equation models. In this paper, we numerically simulate the Susceptible-Infected-Recoverd (SIR) model on Barabási-Albert network and Erdös-Rényi network to analyze the spread of COVID-19 in Pakistan so that we know the severity of the disease. We also show how a situation becomes alarming if hubs in a network get infected.

Physical, psychological and pandemic fatigue in the fourth wave of COVID-19 outbreak in Hong Kong: population-based, cross-sectional study

OBJECTIVES

To examine the physical, psychological and pandemic fatigue during the COVID-19 pandemic, and to explore the correlates of fatigue using a representative, population-based, community sample of Chinese adults in Hong Kong.

DESIGN

Cross-sectional study.

SETTING

Telephone surveys in Hong Kong from December 2020 to January 2021 (during the fourth wave of COVID-19 outbreak).

PARTICIPANTS

1255 adults living in Hong Kong aged 18 years or older sampled using a multistage approach (53% women).

MAIN OUTCOME MEASURES

Physical and psychological fatigue: The Fatigue Assessment Scale (with a cut-off score of 22). Pandemic fatigue: Adherence to six disease prevention measures (DPM) recommended by the government.

RESULTS

About 25.7% of the participants were feeling fatigued physically and psychologically. Physical and psychological fatigue was not directly associated with pandemic fatigue, and their association was fully mediated by self-perceived disruptions of COVID-19-related restrictions in daily life. Results from the hierarchical regression analysis showed that higher levels of fatigue were associated with economically inactive status (B=0.18, SE=0.04, p<0.001), having family or friend infected with COVID-19 before or during the study (B=0.17, SE=0.01, p<0.001), lower acceptability of DPM (B=-0.16, SE=0.03, p<0.001), greater disruptions in daily life (B=0.22, SE=0.02, p<0.001), and greater trust in authorities in ending the pandemic (B=0.11, SE=0.02, p<0.001).

CONCLUSIONS

Fatigue affected many individuals during the pandemic. Disruptions of DPM in daily life may serve as a mediator in the association between pandemic fatigue and physical and psychological fatigue, reflecting the need of effective interventions to reduce self-perceived disruptions of COVID-19-related restrictions in the general population. Resources should be allocated to provide sufficient services to individuals vulnerable to fatigue during the prolonged pandemic.

Changes in Air Quality during the Period of COVID-19 in China

This paper revisits the heterogeneous impacts of COVID-19 on air quality. For different types of Chinese cities, we analyzed the different degrees of improvement in the concentrations of six air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) during COVID-19 by analyzing the predictivity of air quality. Specifically, we divided the sample into three groups: cities with severe outbreaks, cities with a few confirmed cases, and cities with secondary outbreaks. Ensemble empirical mode decomposition (EEMD), recursive plots (RPs), and recursive quantitative analysis (RQA) were used to analyze these heterogeneous impacts and the predictivity of air quality. The empirical results indicated the following: (1) COVID-19 did not necessarily improve air quality due to factors such as the rebound effect of consumption, and its impacts on air quality were short-lived. After the initial outbreak, NO2, CO, and PM2.5 emissions declined for the first 1-3 months. (2) For the cities with severe epidemics, air quality was improved, but for the cities with second outbreaks, air quality was first enhanced and then deteriorated. For the cities with few confirmed cases, air quality first deteriorated and then improved. (3) COVID-19 changed the stability of the air quality sequence. The predictability of the air quality index (AQI) declined in cities with serious epidemic situations and secondary outbreaks, but for the cities with a few confirmed cases, the AQI achieved a stable state sooner. The conclusions may facilitate the analysis of differences in air quality evolution characteristics and fluctuations before and after outbreaks from a quantitative perspective.

SARS-CoV-2 transmission in university classes

We investigate transmission dynamics for SARS-CoV-2 on a real network of classes at Simon Fraser University. Outbreaks are simulated over the course of one semester across numerous parameter settings, including moving classes above certain size thresholds online. Regression trees are used to analyze the effect of disease parameters on simulation outputs. We find that an aggressive class size thresholding strategy is required to mitigate the risk of a large outbreak, and that transmission by symptomatic individuals is a key driver of outbreak size. These findings provide guidance for designing control strategies at other institutions, as well as setting priorities and allocating resources for disease monitoring.

A general framework for optimising cost-effectiveness of pandemic response under partial intervention measures

The COVID-19 pandemic created enormous public health and socioeconomic challenges. The health effects of vaccination and non-pharmaceutical interventions (NPIs) were often contrasted with significant social and economic costs. We describe a general framework aimed to derive adaptive cost-effective interventions, adequate for both recent and emerging pandemic threats. We also quantify the net health benefits and propose a reinforcement learning approach to optimise adaptive NPIs. The approach utilises an agent-based model simulating pandemic responses in Australia, and accounts for a heterogeneous population with variable levels of compliance fluctuating over time and across individuals. Our analysis shows that a significant net health benefit may be attained by adaptive NPIs formed by partial social distancing measures, coupled with moderate levels of the society's willingness to pay for health gains (health losses averted). We demonstrate that a socially acceptable balance between health effects and incurred economic costs is achievable over a long term, despite possible early setbacks.

What have we learned about socioeconomic inequalities in the spread of COVID-19? A systematic review

This article aims to provide a better understanding of the associations between groups of socioeconomic variables and confirmed cases of COVID-19. The focus is on cross-continental differences of reported positive, negative, unclear, or no associations. A systematic review of the literature is conducted on the Web of Science and SCOPUS databases. Our search identifies 314 eligible studies published on or before 31 December 2021. We detect nine groups of frequently used socioeconomic variables and results are presented by region of the world (Africa, Asia, Europe, Middle East, North American and South America). The review expands to describe the most used statistical and modelling techniques as well as inclusion of additional dimensions such as demographic, healthcare weather and mobility. Meanwhile findings agree on the generalized positive impact of population density, per capita GDP and urban areas on transmission of infections, contradictory results have been found concerning to educational level and income.

Mycobacterium tuberculosis Transmission in High-Incidence Settings-New Paradigms and Insights

Tuberculosis has affected humankind for thousands of years, but a deeper understanding of its cause and transmission only arose after Robert Koch discovered Mycobacterium tuberculosis in 1882. Valuable insight has been gained since, but the accumulation of knowledge has been frustratingly slow and incomplete for a pathogen that remains the number one infectious disease killer on the planet. Contrast that to the rapid progress that has been made in our understanding SARS-CoV-2 (the cause of COVID-19) aerobiology and transmission. In this Review, we discuss important historical and contemporary insights into M. tuberculosis transmission. Historical insights describing the principles of aerosol transmission, as well as relevant pathogen, host and environment factors are described. Furthermore, novel insights into asymptomatic and subclinical tuberculosis, and the potential role this may play in population-level transmission is discussed. Progress towards understanding the full spectrum of M. tuberculosis transmission in high-burden settings has been hampered by sub-optimal diagnostic tools, limited basic science exploration and inadequate study designs. We propose that, as a tuberculosis field, we must learn from and capitalize on the novel insights and methods that have been developed to investigate SARS-CoV-2 transmission to limit ongoing tuberculosis transmission, which sustains the global pandemic.

Can infectious modelling be applicable globally - lessons from COVID 19

Contagious diseases are needed to be monitored to prevent spreading within communities. Timely advice and predictions are necessary to overcome the consequences of those epidemics. Currently, emphasis has been placed on computer modelling to achieve the needed forecasts, the best example being the COVID-19 pandemic. Scientists used various models to determine how diverse sociodemographic factors correlated and influenced COVID-19 Global transmission and demonstrated the utility of computer models as tools in disease management. However, as modelling is done with assumptions with set rules, calculating uncertainty quantification is essential in infectious modelling when reporting the results and trustfully describing the limitations. This article summarizes the infectious disease modelling strategies, challenges, and global applicability by focusing on the COVID-19 pandemic.

The CP-ABM approach for modelling COVID-19 infection dynamics and quantifying the effects of non-pharmaceutical interventions

The motivation for this research is to develop an approach that reliably captures the disease dynamics of COVID-19 for an entire population in order to identify the key events driving change in the epidemic through accurate estimation of daily COVID-19 cases. This has been achieved through the new CP-ABM approach which uniquely incorporates Change Point detection into an Agent Based Model taking advantage of genetic algorithms for calibration and an efficient infection centric procedure for computational efficiency. The CP-ABM is applied to the Northern Ireland population where it successfully captures patterns in COVID-19 infection dynamics over both waves of the pandemic and quantifies the significant effects of non-pharmaceutical interventions (NPI) on a national level for lockdowns and mask wearing. To our knowledge, there is no other approach to date that has captured NPI effectiveness and infection spreading dynamics for both waves of the COVID-19 pandemic for an entire country population.

The effects of air transport mobility and global connectivity on viral transmission: Lessons learned from Covid-19 and its variants

We investigate the impact of air travel mobility and global connectivity on viral transmission by tracing the announced arrival time of COVID-19 and its major variants in countries around the world. We find that air travel intensity to a country, "effective distance" as measured by international air traffic, is generally a significant predictor for the announced viral arrival time. The level of healthcare infrastructure in a country is less important at predicting the initial transmission and detection time of a virus. A policy variable, notably the percentage reduction of total inbound seats in response to a viral outbreak, is largely ineffective at delaying viral transmission and discovery time. These findings suggest that air network connectivity is a major contributor to the speed of viral transmission. However, government attempts to delay viral transmission by reducing air network connectivity after the virus is first discovered are largely ineffective.

Impact of school closures and reopening on COVID-19 caseload in 6 cities of Pakistan: An Interrupted Time Series Analysis

Schools were closed all over Pakistan on November 26, 2020 to reduce community transmission of COVID-19 and reopened between January 18 and February 1, 2021. However, these closures were associated with significant economic and social costs, prompting a review of effectiveness of school closures to reduce the spread of COVID-19 infections in a developing country like Pakistan. A single-group interrupted time series analysis (ITSA) was used to measure the impact of school closures, as well as reopening schools, on daily new COVID-19 cases in 6 major cities across Pakistan: Lahore, Karachi, Islamabad, Quetta, Peshawar, and Muzaffarabad. However, any benefits were contingent on continued closure of schools, as cases bounced back once schools reopened. School closures are associated with a clear and statistically significant reduction in COVID-19 cases by 0.07 to 0.63 cases per 100,000 population, while reopening schools is associated with a statistically significant increase. Lahore is an exception to the effect of school closures, but it too saw an increase in COVID-19 cases after schools reopened in early 2021. We show that closing schools was a viable policy option, especially before vaccines became available. However, its social and economic costs must also be considered.

Assessing COVID-19 vaccination strategies in varied demographics using an individual-based model

Background

New variants of SARS-CoV-2 are constantly discovered. Administration of COVID-19 vaccines and booster doses, combined with the application of non-pharmaceutical interventions (NPIs), is often used to prevent outbreaks of emerging variants. Such outbreak dynamics are further complicated by the population's behavior and demographic composition. Hence, realistic simulations are needed to estimate the efficiency of proposed vaccination strategies in conjunction with NPIs.

Methods

We developed an individual-based model of COVID-19 dynamics that considers age-dependent parameters such as contact matrices, probabilities of symptomatic and severe disease, and households' age distribution. As a case study, we simulate outbreak dynamics under the demographic compositions of two Israeli cities with different household sizes and age distributions. We compare two vaccination strategies: vaccinate individuals in a currently prioritized age group, or dynamically prioritize neighborhoods with a high estimated reproductive number. Total infections and hospitalizations are used to compare the efficiency of the vaccination strategies under the two demographic structures, in conjunction with different NPIs.

Results

We demonstrate the effectiveness of vaccination strategies targeting highly infected localities and of NPIs actively detecting asymptomatic infections. We further show that different optimal vaccination strategies exist for each sub-population's demographic composition and that their application is superior to a uniformly applied strategy.

Conclusion

Our study emphasizes the importance of tailoring vaccination strategies to subpopulations' infection rates and to the unique characteristics of their demographics (e.g., household size and age distributions). The presented simulation framework and findings can help better design future responses against the following emerging variants.

Spatial heterogeneity affects predictions from early-curve fitting of pandemic outbreaks: a case study using population data from Denmark

The modelling of pandemics has become a critical aspect in modern society. Even though artificial intelligence can help the forecast, the implementation of ordinary differential equations which estimate the time development in the number of susceptible, (exposed), infected and recovered (SIR/SEIR) individuals is still important in order to understand the stage of the pandemic. These models are based on simplified assumptions which constitute approximations, but to what extent this are erroneous is not understood since many factors can affect the development. In this paper, we introduce an agent-based model including spatial clustering and heterogeneities in connectivity and infection strength. Based on Danish population data, we estimate how this impacts the early prediction of a pandemic and compare this to the long-term development. Our results show that early phase SEIR model predictions overestimate the peak number of infected and the equilibrium level by at least a factor of two. These results are robust to variations of parameters influencing connection distances and independent of the distribution of infection rates.

Does the internet help governments contain the COVID-19 pandemic? Multi-country evidence from online human behaviour

The effectiveness of social distancing and other public health interventions for containing the COVID-19 pandemic has been demonstrated. However, whether and how Internet use behaviours can lead to enhanced self-protection and reduced transmission when considered in conjunction with behavioural interventions remains unclear. This study investigated the strength of effective Internet behaviours and its interaction with global public health interventions for controlling the COVID-19 pandemic. We conducted an econometric analysis of multisource infection and policy information, Internet behaviour, and meteorological information from worldwide in a 3-month period. People's Internet behaviours may contribute crucially to pandemic containment. Furthermore, they may help enhance the effects of public health interventions, particularly behavioural interventions. We discussed plausible mechanisms through which Internet behaviours reduce epidemic spread independently or in tandem with behavioural interventions. Further investigation into the heterogeneity of the interventions demonstrates Internet behaviour's significance in heightening the effects of difficult-to-implement, primitive crisis orientation, and specific objectives of interventions. Governments should recognise the importance of the Internet and leverage it in managing social crises. Our findings serve as a reference for the formulation of global public health policy. Specifically, the insights provided herein can facilitate the implementation of strategies for containing ongoing secondary outbreaks of COVID-19 or outbreaks of other emergent infectious diseases.