Evaluating the Disaster Risk of the COVID-19 Pandemic Using an Ecological Niche Model

Abstract

Since 2019, the novel coronavirus has spread rapidly worldwide, greatly affecting social stability and human health. Pandemic prevention has become China’s primary task in responding to the transmission of COVID-19. Risk mapping and the proposal and implementation of epidemic prevention measures emphasize many research efforts. In this study, we collected location information for confirmed COVID-19 cases in Beijing, Shenyang, Dalian, and Shijiazhuang from 5 October 2020 to 5 January 2021, and selected 15 environmental variables to construct a model that comprehensively considered the parameters affecting the outbreak and spread of COVID-19 epidemics. Annual average temperature, catering, medical facilities, and other variables were processed using ArcGIS 10.3 and classified into three groups, including natural environmental variables, positive socio-environmental variables, and benign socio-environmental variables. We modeled the epidemic risk distribution for each area using the MaxEnt model based on the case occurrence data and environmental variables in four regions, and evaluated the key environmental variables influencing the epidemic distribution. The results showed that medium-risk zones were mainly distributed in Changping and Shunyi in Beijing, while Huanggu District in Shenyang and the southern part of Jinzhou District and the eastern part of Ganjingzi District in Dalian also represented areas at moderate risk of epidemics. For Shijiazhuang, Xinle, Gaocheng and other places were key COVID-19 epidemic spread areas. The jackknife assessment results revealed that positive socio-environmental variables are the most important factors affecting the outbreak and spread of COVID-19. The average contribution rate of the seafood market was 21.12%, and this contribution reached as high as 61.3% in Shenyang. The comprehensive analysis showed that improved seafood market management, strengthened crowd control and information recording, industry-catered specifications, and well-trained employees have become urgently needed prevention strategies in different regions. The comprehensive analysis indicated that the niche model could be used to classify the epidemic risk and propose prevention and control strategies when combined with the assessment results of the jackknife test, thus providing a theoretical basis and information support for suppressing the spread of COVID-19 epidemics.

Multi-Source Data and Machine Learning-Based Refined Governance for Responding to Public Health Emergencies in Beijing: A Case Study of COVID-19

“…In addition, several studies have used structured Gaussian models with demographic, socioeconomic, and political data and demographic information as the computational framework to predict regional risk for U.S. counties [24] or the Cantabria region of Spain at the community level [25]. Maxent machine learning models have also been widely used for the evaluation of regional epidemic risk levels [26,27]. In addition, a portion of research has also focused on a specific element for risk prediction, with building density [28] and human activity patterns [29] being used to determine and identify potential site transmission risk in cities.…”

“…After combining different studies [22,23,26,27], we decided to quantify epidemic by the number of COVID-19 cases present in each statistical un collected the geographic location of COVID-19 cases from January 2022 (Om introduced to Beijing [39]) until 15 October 2022, taken from the Beijing Mu and Wellness Commission (http://wjw.Beijing.gov.cn/ (accessed on 15 Octo ter the geographic locations of the cases had been statistically organized, the longitude coordinates were determined using Baidu Maps and converted in Meanwhile, this study used ArcGIS software to establish a 500 m × 50 hood-scale statistical fishing network for Beijing (according to the 15-min liv posed by the Shanghai Municipal Government in 2016 to delineate the sc indicated the number of new virus cases within each table with a 500 m se determine the existing epidemic risk index within each statistical unit. In risk units were identified.…”

The Impact of COVID-19 on Bank Sector Traditional Business Model Sustainability in China: Bank Branch Versus Fintech

“…In this paper, the number of tourists in the region is chosen as the instrumental variable because COVID-19 is highly contagious [44,45], and bank branches do not significantly affect the number of tourists in the region, satisfying the first condition of the instrumental variable. On the other hand, tourists would accelerate the spread of the virus and increase the number of confirmed COVID-19 diagnoses, satisfying the second condition of the instrumental variable.…”

Constructing big data prevention and control model for public health emergencies in China: A grounded theory study

“…( 2 – 4 ). Since the 21st century, global public health emergencies have occurred frequently: the Severe Acute Respiratory Syndrome (SARS) outbreak in 2003 ( 5 ), Influenza A (H1N1) outbreak in 2009 ( 6 ), Middle East Respiratory Syndrome (MERS) outbreak in 2015 ( 7 ), and novel coronavirus (COVID-19) outbreak in 2019 ( 8 ). Frequent public health emergencies have caused significant damage to people's lives and health as well as the operation of the economy and society ( 9 ).…”

“…in 2009 (6), Middle East Respiratory Syndrome (MERS) outbreak in 2015 (7), and novel coronavirus (COVID-19) outbreak in 2019 (8). Frequent public health emergencies have caused significant damage to people's lives and health as well as the operation of the economy and society (9).…”