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volume 43, issue 1, january 2023
1. title: effects of air pollution and weather on the initial covid-19 outbreaks in united states, italy, spain, and china: a comparative study
authors: alberto j. alaniz, mario a. carvajal, jorge g. carvajal, pablo m. vergara
abstract: contrasting effects have been identified in association of weather (temperature and humidity) and pollutant gases with covid-19 infection, which could be derived from the influence of lockdowns and season change. the influence of pollutant gases and climate during the initial phases of the pandemic, before the closures and the change of season in the northern hemisphere, is unknown. here, we used a spatial-temporal bayesian zero-inflated-poisson model to test for short-term associations of weather and pollutant gases with the relative risk of covid-19 disease in china (first outbreak) and the countries with more cases during the initial pandemic (the united states, spain and italy), considering also the effects of season and lockdown. we found contrasting association between pollutant gases and covid-19 risk in the united states, italy, and spain, while in china it was negatively associated (except for so2). covid-19 risk was positively associated with specific humidity in all countries, while temperature presented a negative effect. our findings showed that short-term associations of air pollutants with covid-19 infection vary strongly between countries, while generalized effects of temperature (negative) and humidity (positive) with covid-19 was found. our results show novel information about the influence of pollution and weather on the initial outbreaks, which contribute to unravel the mechanisms during the beginning of the pandemic.
2. title: analyzing factors contributing to covid-19 mortality in the united states using artificial intelligence techniques
authors: mehrab tanhaeean, negin nazari, seyed hosein iranmanesh, majid abdollahzade
abstract: having started since late 2019, covid-19 has spread through far many nations around the globe. not being known profoundly, the novel virus of the coronaviruses family has already caused more than half a million deaths and put the lives of many more people in danger. policymakers have implemented preventive measures to curb the outbreak of the virus, and health practitioners along with epidemiologists have pointed out many social and hygienic factors associated with the virus incidence and mortality. however, a clearer vision of how the various factors cited hitherto can affect total death in different communities is yet to be analyzed. this study has put this issue forward. applying artificial intelligence techniques, the relationship between covid-19 death toll and determinants mentioned as strongly influential in earlier studies was investigated. in the first stage, employing best-worst method, the weight of the primer contributing factor, effectiveness of strategies, was estimated. then, using an integrated best-worst method�local linear neuro-fuzzy�adaptive neuro-fuzzy inference system approach, the relationship between covid-19 mortality rate and all factors namely effectiveness of strategies, age pyramid, health system status, and community health status was elucidated more specifically.
3. title: interactions among the human and organizational factors within the public sector regarding epidemic prevention and control
authors: lipeng fu, xueqing wang, shuyi deng, shaopeng cao, heng zhao
abstract: the management of human and organizational factors (hofs) within the public sector directly concerns the efficacy of epidemic prevention and control (epc). insufficient examination of such hofs has led to defective countermeasures. this study attempts to comprehensively identify the hofs within the public sector critical to epc and investigate their interactions with the weighted network theory. a total of 55 hofs were identified, and their interactions were assessed and visualized in the chinese context. then, the established weighted network was analyzed to investigate the interactions and diagnose critical factors and sectors. the analysis shows that there are strong interactions among hofs, and that the human and organizational risks emerging from administrative departments of public health, centers for disease control and prevention, and medical institutions act as the key risk sources in the complex interconnected epc system, exacerbating risk and causing a significant domino effect. it is recommended that the authorities devote more resources to the core sectors and endeavor to reinforce those critical hofs by implementing closer risk communication, collaboration, and response. this study may deepen and broaden the authorities� awareness and understanding of interactions among hofs regarding epidemic mitigation, and strengthen their capacity to perceive, evaluate, and manage these factors in a proactive and effective way, thereby facilitating the success of epc.
4. title: optimal control analysis of a multigroup seaihrd model for covid-19 epidemic
authors: kai zong, cuicui luo
abstract: the covid-19 pandemic has threatened public health and caused substantial economic loss to most countries worldwide. a multigroup susceptible�exposed�asymptomatic�infectious�hospitalized�recovered�dead (seaihrd) compartment model is first constructed to model the spread of the disease by dividing the population into three age groups: young (aged 0�19), prime (aged 20�64), and elderly (aged 65 and over). then, we develop a free terminal time, partially fixed terminal state optimal control problem to minimize deaths and costs associated with hospitalization and the implementation of different control strategies. and the optimal strategies are derived under different assumptions about medical resources and vaccination. specifically, we explore optimal control strategies for reaching herd immunity in the covid-19 outbreak in a free terminal time situation to evaluate the effect of nonpharmaceutical interventions (npis) and vaccination as control measures. the transmission rate of sars-cov-2 is calibrated by using real data in the united states at the early stage of the epidemic. through numerical simulation, we conclude that the outbreak of covid-19 can be contained by implementing appropriate control of the prime age population and relatively strict control measures for young and elderly populations. within a specific period, strict control measures should be implemented before the vaccine is marketed.
5. title: a framework for supporting health capability-based planning: identifying and structuring health capabilities
authors: marcelo zawadzki, gilberto montibeller
abstract: the covid-19 pandemic has highlighted that health security systems must be redesigned, in a way that they are better prepared and ready to cope with multiple and diverse health threats, from predictable and well-known epidemics to unexpected and challenging pandemics. a powerful way of accomplishing this goal is to focus the planning on health capabilities. this focus may enhance the ability to respond to and recover from health threats and emergencies, while helping to identify the level of resources required to maintain and build up those capabilities that are critical in ensuring the preparedness of health security systems. however, current attempts for defining and organizing health capabilities have some important limitations. first, such attempts were not designed to consider diverse scenarios and multiple health threats. second, they provide a limited representation of capabilities and lack a systemic perspective. third, they struggle to identify capability and resource gaps. in this article, we thus propose a new framework for identifying and structuring health capabilities and support health capability planning. the suggested framework has three main potential benefits. first, the framework may help policymakers in planning under high levels of uncertainty, by considering multiple realistic and stressful scenarios. second, it can provide risk analysts with a more comprehensive representation of health capabilities and their complex relationships. third, it can support planners in identifying resource and capability gaps. we illustrate the use of the framework in practice considering an outbreak scenario caused by three different health threats (covid-19, ebola, and influenza viruses).
6. title: health insurance system and resilience to epidemics
authors: jimin hong, sung hun seog
abstract: we theoretically analyze the resilience (efficiency) of health insurance systems and diverse factors including trace and test technology, infection and contagion rates, and social distancing/lockdown policy, in coping with contagious diseases like covid-19. our findings can be summarized as follows. first, public insurance is more resilient than market insurance, as the former's investment in test technology is made at the social optimum, whereas the latter's investment is less. the decentralized behavior of competing insurers leads to a less resilient outcome. second, resilience decreases as the market becomes more competitive because the externality effect becomes more severe. third, a higher contagion rate, a more cost-efficient test technology or a higher initial infection rate unless it is not too high, leads to a higher test accuracy level. fourth, the socially optimal social distancing/lockdown policy is determined by comparison between its relative costs and the benefit from contagion reduction.
7. title: community centered public safety resilience under public emergencies: a case study of covid-19
authors: jiaxin zhang, guoqing zha, xing pan, dujun zuo, qianxin xu, huixiong wang
abstract: during public emergencies, the level of public safety will be resilient and follow a process from decline to rise. regarding the concept and influencing factors of public safety resilience, a three-level public safety resilience framework that includes personal, community, and government levels was proposed in this study. it provided the overall metrics that used the resistance and recovery ability to describe the dynamic characteristics of public safety resilience as well as the resilience assessment indexes on three levels. in the context of the coronavirus disease 2019 (covid-19) pandemic, this study applied the proposed framework in a case study on public safety resilience at the beihang community, beijing, china through descriptive statistics, structural equation model, and principal component regression analysis of questionnaire data. the data analysis results showed that community resilience was the most important of the three levels of public safety resilience. in addition, community resilience could improve personal resilience, and government resilience had a positive effect on community and personal resilience. compared with the resistance ability, the recovery ability was influenced more by the operation and improvement of the community. this study is conducive to understanding and improving public safety resilience on the personal, community, and government levels and can help relevant parties improve their ability to respond to the covid-19 pandemic. furthermore, the methods used in this study can be extended to other studies on public emergencies.
8. title: seir model to address the impact of face masks amid covid-19 pandemic
authors: ahmed maged, abdullah ahmed, salah haridy, arthur w. baker, min xie
abstract: early in the pandemic of coronavirus disease 2019 (covid-19), face masks were used extensively by the general public in several asian countries. the lower transmission rate of the severe acute respiratory syndrome coronavirus 2 (sars-cov-2) in asian countries compared with western countries suggested that the wider community use of face masks has the potential to decrease transmission of sars-cov-2. a risk assessment model named susceptible, exposed, infectious, recovered (seir) model is used to quantitatively evaluate the potential impact of community face masks on sars-cov-2 reproduction number (r0) and peak number of infectious persons. for a simulated population of one million, the model showed a reduction in r0 of 49% and 50% when 60% and 80% of the population wore masks, respectively. moreover, we present a modified model that considers the effect of mask-wearing after community vaccination. interestingly mask-wearing still provided a considerable benefit in lowering the number of infectious individuals. the results of this research are expected to help public health officials in making prompt decisions involving resource allocation and crafting legislation.
9. title: an seir model with time-varying coefficients for analyzing the sars-cov-2 epidemic
authors: paolo girardi, carlo gaetan
abstract: in this study, we propose a time-dependent susceptible-exposed-infected-recovered (seir) model for the analysis of the sars-cov-2 epidemic outbreak in three different countries, the united states, italy, and iceland using public data inherent the numbers of the epidemic wave. since several types and grades of actions were adopted by the governments, including travel restrictions, social distancing, or limitation of movement, we want to investigate how these measures can affect the epidemic curve of the infectious population. the parameters of interest for the seir model were estimated employing a composite likelihood approach. moreover, standard errors have been corrected for temporal dependence. the adoption of restrictive measures results in flatten epidemic curves, and the future evolution indicated a decrease in the number of cases.
10. title: a novel textual track-data-based approach for estimating individual infection risk of covid-19
authors: lu wei, xiaojing li, zhongbo jing, zhidong liu
abstract: with the recurrence of infectious diseases caused by coronaviruses, which pose a significant threat to human health, there is an unprecedented urgency to devise an effective method to identify and assess who is most at risk of contracting these diseases. china has successfully controlled the spread of covid-19 through the disclosure of track data belonging to diagnosed patients. this paper proposes a novel textual track-data-based approach for individual infection risk measurement. the proposed approach is divided into three steps. first, track features are extracted from track data to build a general portrait of covid-19 patients. then, based on the extracted track features, we construct an infection risk indicator system to calculate the infection risk index (iri). finally, individuals are divided into different infection risk categories based on the iri values. by doing so, the proposed approach can determine the risk of an individual contracting covid-19, which facilitates the identification of high-risk populations. thus, the proposed approach can be used for risk prevention and control of covid-19. in the empirical analysis, we comprehensively collected 9455 pieces of track data from 20 january 2020 to 30 july 2020, covering 32 provinces/provincial municipalities in china. the empirical results show that the chinese covid-19 patients have six key features that indicate infection risk: place, region, close-contact person, contact manner, travel mode, and symptom. the iri values for all 9455 patients vary from 0 to 43.19. individuals are classified into the following five infection risk categories: low, moderate-low, moderate, moderate-high, and high risk.
11. title: a probabilistic epidemiological model for infectious diseases: the case of covid-19 at global-level
authors: heitor oliveira duarte, paulo gabriel siqueira, alexandre calumbi antunes oliveira, m�rcio das chagas moura
abstract: this study has developed a probabilistic epidemiological model a few weeks after the world health organization declared covid-19 a pandemic (based on the little data available at that time). the aim was to assess relative risks for future scenarios and evaluate the effectiveness of different management actions for 1 year ahead. we quantified, categorized, and ranked the risks for scenarios such as business as usual, and moderate and strong mitigation. we estimated that, in the absence of interventions, covid-19 would have a 100% risk of explosion (i.e., more than 25% infections in the world population) and 34% (2.6 billion) of the world population would have been infected until the end of simulation. we analyzed the suitability of model scenarios by comparing actual values against estimated values for the first 6 weeks of the simulation period. the results proved to be more suitable with a business-as-usual scenario in asia and moderate mitigation in the other continents. if everything went on like this, we would have 55% risk of explosion and 22% (1.7 billion) of the world population would have been infected. strong mitigation actions in all continents could reduce these numbers to, 7% and 3% (223 million), respectively. although the results were based on the data available in march 2020, both the model and probabilistic approach proved to be practicable and could be a basis for risk assessment in future pandemic episodes with unknown virus, especially in the early stages, when data and literature are scarce.
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