Critical appraisal of the Coronavirus preparedness among countries using the global health security index

Introduction: This critical study was aimed to investigate the utility of the Global Health Security Index in predicting the current COVID-19 responses. Material and Methods: Number of infected patients, deaths, incidence and the death rate per 100,000 populations related to 55 countries per week for 26 weeks were extracted. The relationship of GHSI scores and country preparedness for the pandemic was compared. Results: According to the GHSI, the incidence rate in most prepared countries was higher than the incidence rate in the more prepared countries, and which was higher than the incidence rate in the least prepared countries. However, Prevention, Detection and reporting, Rapid response, Health system, compliance with international norms and Risk environment, as well as Overall, the incidence and death rate per 100,000 people have not been like this. Conclusion: Due to mismatch between the GHSI score and fact about COVID-19 incidence, it seems necessary to investigate the factors involved in this discrepancy. © 2022, Published by Frontiers in Health Informatics.

The Role of Economic Stress, Health Concerns, and Institutional Trust in Supporting Public Protests against COVID-19 Lockdown Measures in Denmark.

During the current COVID-19 pandemic, most governments around the world have adopted strict COVID-19 lockdown measures. In Denmark, mainly from January to March 2021, an anonymous protest group called Men in Black organized demonstrations against the Danish COVID-19 lockdown measures in the three major cities in Denmark. Based on an online survey that we carried out in March 2021 in the Danish population aged 16 years and above (n = 2692), we analyze the individual-level factors behind supporting these demonstrations. Based on ordered logit regressions, the results show that being Muslim and being self-employed (business owner) was positively related to supporting the demonstrations, and that age and living in a city municipality was negatively related to supporting the demonstrations. Based on structural equation modeling (SEM), the results showed that the municipal COVID-19 incidence rate mediates the effect of living in a city municipality, that institutional trust mediates the effect of being Muslim, and that COVID-19 health concerns and institutional trust mediate the effect of age. Overall, economic stress among business owners, health concerns, and institutional trust were found to be the main predictors of supporting the demonstrations against the COVID-19 lockdown measures in Denmark.

Global investigation for an "SIS" model for COVID-19 epidemic with asymptomatic infection.

In this paper, we analyse a dynamical system taking into account the asymptomatic infection and we consider optimal control strategies based on a regular network. We obtain basic mathematical results for the model without control. We compute the basic reproduction number (R) by using the method of the next generation matrix then we analyse the local stability and global stability of the equilibria (disease-free equilibrium (DFE) and endemic equilibrium (EE)). We prove that DFE is LAS (locally asymptotically stable) when R<1 and it is unstable when R>1. Further, the existence, the uniqueness and the stability of EE is carried out. We deduce that when R>1, EE exists and is unique and it is LAS. By using generalized Bendixson-Dulac theorem, we prove that DFE is GAS (globally asymptotically stable) if R<1 and that the unique endemic equilibrium is globally asymptotically stable when R>1. Later, by using Pontryagin's maximum principle, we propose several reasonable optimal control strategies to the control and the prevention of the disease. We mathematically formulate these strategies. The unique optimal solution was expressed using adjoint variables. A particular numerical scheme was applied to solve the control problem. Finally, several numerical simulations that validate the obtained results were presented.

The Risk of SARS-CoV-2 Infection in Pregnant Women: An Observational Cohort Study Using the BIFAP Database.

BACKGROUND: It has been suggested that women experiencing during pregnancy several physiological and immunological changes that might increase the risk of any infection including the SARS-CoV-2. OBJECTIVE: We aimed to quantify the risk of SARS-CoV-2 infection during pregnancy compared with women with no pregnancies. METHODS: We used data from the BIFAP database and a published algorithm to identify all pregnancies during 2020. Pregnancies were matched (1:4) by age region, and length of pregnancy with a cohort of women of childbearing age. All women with SARS-CoV-2 infection before entering the study were discarded. We estimated incidence rates of SARS-CoV-2 with 95% confidence intervals (CIs) expressed by 1000 person-months as well as Kaplan-Meier figures overall and also stratified according to pregnancy period: during pregnancy, at puerperium (from end of pregnancy up to 42 days) and after pregnancy. (from 43 days after pregnancy up to end pf study period (i.e., June 2021). We conducted a Cox regression to assess risk factors for SARS-COV infection. The incidence rate of SARS-CoV-2 infection expressed by 1000 person-months were. RESULTS: There was a total of 103,185 pregnancies and 412,740 matched women at childbearing, with a mean age of 32.3 years. The corresponding incidence rates of SARS-CoV-2 infection according to cohorts were: 2.44 cases per 1000 person-months (confidence interval (CI) 95%: 2.40-2.50) and 4.29 (95% CI: 4.15-4.43) for comparison cohort. The incidence rate ratio (IRR) of SARS-CoV-2 was 1.76 (95% CI: 1.69-1.83). When analyzing according to pregnancy period, the IRRs were 1.30 (95% CI: 11.20-1.41) during the puerperium and 1.19 (95% CI: 41.15-1.23) after pregnancy. In addition to pregnancy itself, other important risk factors were obesity (1.33 (95% CI: 1.23-1.44)) and diabetes (1.23 (95% CI: 11.00-1.50). CONCLUSION: Pregnant women are at increased risk of SARS-CoV-2 infection compared with women of childbearing age not pregnant. Nevertheless, there is a trend towards reverting during puerperium and after pregnancy.

Incidence, Prevalence, and Sources of COVID-19 Infection among Healthcare Workers in Hospitals in Malaysia.

The COVID-19 pandemic introduced significant novel risks for healthcare workers and healthcare services. This study aimed to determine the prevalence, trends, characteristics, and sources of COVID-19 infection among healthcare workers during the early COVID-19 pandemic in Malaysian hospitals. A cross-sectional study used secondary data collected from a COVID-19 surveillance system for healthcare workers between January and December 2020. Two surges in COVID-19 cases among healthcare workers in Malaysia were epidemiologically correlated to a similarly intense COVID-19 pattern of transmission in the community. The period prevalence of COVID-19 infection and the mortality rate among healthcare workers in Malaysia were 1.03% and 0.0019%, respectively. The majority of infections originated from the workplace (53.3%); a total of 36.3% occurred among staff; a total of 17.0% occurred between patients and staff; and 43.2% originated from the community. Healthcare workers had a 2.9 times higher incidence risk ratio for the acquisition of COVID-19 infection than the general population. Nursing professionals were the most highly infected occupational group (40.5%), followed by medical doctors and specialists (24.1%), and healthcare assistants (9.7%). The top three departments registering COVID-19 infections were the medical department (23.3%), the emergency department (17.7%), and hospital administration and governance (9.1%). Occupational safety and health units need to be vigilant for the early detection of a disease outbreak to prevent the avoidable spread of disease in high-risk settings. The transformation of some tertiary hospitals to dedicated COVID-19 care, the monitoring of new procedures for the management of COVID-19 patients, and appropriate resource allocation are key to successful risk mitigation strategies.

Effects of Meteorological Factors and Air Pollutants on COVID-19 Transmission under the Action of Control Measures.

At present, COVID-19 is still spreading, and its transmission patterns and the main factors that affect transmission behavior still need to be thoroughly explored. To this end, this study collected the cumulative confirmed cases of COVID-19 in China by 8 April 2020. Firstly, the spatial characteristics of the COVID-19 transmission were investigated by the spatial autocorrelation method. Then, the factors affecting the COVID-19 incidence rates were analyzed by the generalized linear mixed effect model (GLMMs) and geographically weighted regression model (GWR). Finally, the geological detector (GeoDetector) was introduced to explore the influence of interactive effects between factors on the COVID-19 incidence rates. The results showed that: (1) COVID-19 had obvious spatial aggregation. (2) The control measures had the largest impact on the COVID-19 incidence rates, which can explain the difference of 34.2% in the COVID-19 incidence rates, while meteorological factors and pollutant factors can only explain the difference of 1% in the COVID-19 incidence rates. It explains that some of the literature overestimates the impact of meteorological factors on the spread of the epidemic. (3) The influence of meteorological factors was stronger than that of air pollution factors, and the interactive effects between factors were stronger than their individual effects. The interaction between relative humidity and NO2 was stronger. The results of this study will provide a reference for further prevention and control of COVID-19.

Factors Influencing Background Incidence Rate Calculation: Systematic Empirical Evaluation Across an International Network of Observational Databases.

Objective: Background incidence rates are routinely used in safety studies to evaluate an association of an exposure and outcome. Systematic research on sensitivity of rates to the choice of the study parameters is lacking. Materials and Methods: We used 12 data sources to systematically examine the influence of age, race, sex, database, time-at-risk, season and year, prior observation and clean window on incidence rates using 15 adverse events of special interest for COVID-19 vaccines as an example. For binary comparisons we calculated incidence rate ratios and performed random-effect meta-analysis. Results: We observed a wide variation of background rates that goes well beyond age and database effects previously observed. While rates vary up to a factor of 1,000 across age groups, even after adjusting for age and sex, the study showed residual bias due to the other parameters. Rates were highly influenced by the choice of anchoring (e.g., health visit, vaccination, or arbitrary date) for the time-at-risk start. Anchoring on a healthcare encounter yielded higher incidence comparing to a random date, especially for short time-at-risk. Incidence rates were highly influenced by the choice of the database (varying by up to a factor of 100), clean window choice and time-at-risk duration, and less so by secular or seasonal trends. Conclusion: Comparing background to observed rates requires appropriate adjustment and careful time-at-risk start and duration choice. Results should be interpreted in the context of study parameter choices.

Towards building a tuberculosis free world.

The Honourable Prime Minister of India set a target of year 2025 for elimination of TB from the country, 5 years ahead of the Sustainable Development Goal of 2030. Last few years, India has made significant improvements, towards elimination of tuberculosis from the country in the form of bold policies and unprecedented political commitment. While COVID-19 has resulted in setbacks for TB elimination efforts, it has also offered an opportunity to revisit and structurally redesign the public health infrastructure/system in our country. The dream of TB elimination is possible with active participation of all stakeholders and community at large coupled with accelerated development of new diagnostics, drugs, and development of a new TB vaccine. COVID-19 pandemic has shown that vaccines can be developed in a year, contrarily, the lack of a TB vaccine is deterrent in the efforts towards a TB free world. A progress towards TB elimination would require potential contribution of novel TB vaccine. Now, is the time for mobilization towards a TB vaccine to make an impact towards our end TB goal.

Risk Measurement of Perinatal and Neonatal Morbidity Characteristics and Applicability of GAIA Case Definitions: Results and Lessons Learnt of a Hospital-Based Prospective Cohort Study in the Valencia Region (2019-2020).

Post-marketing safety surveillance of new vaccines aimed to be administered during pregnancy is crucial to orchestrate efficient adverse events evaluation. This is of special relevance in the current landscape of new vaccines being introduced in the pregnant women population, and particularly due to the recent administration of COVID-19 vaccines in pregnant women. This multi-center prospective cohort study, nested within the WHO-Global Vaccine Safety-MCC study, involved two hospitals in the Valencia region. Hereby, the incidence rates of seven perinatal and neonatal outcomes in the Valencia region are presented. The pooled data analysis of the two Valencian hospitals allowed the estimation of incidence rates in the Valencia Region (per 1000 live births): 86.7 for low birth weight, 78.2 for preterm birth, 58.8 for small for gestational age, 13 for congenital microcephaly, 0.4 for stillbirth, 1.2 for neonatal death and 6.5 for neonatal infection. These figures are in line with what is expected from a high-income country and the previously reported rates for Spain and Europe, except for the significantly increased rate for congenital microcephaly. Regarding the data for maternal immunization, the vaccination status was collected for 94.4% of the screened pregnant women, highlighting the high quality of the Valencian Vaccine Registry. The study also assessed the Valencian hospitals' capacity for identifying and collecting data on maternal immunization status, as well as the applicability of the GAIA definitions to the identified outcomes.

Background incidence rates of adverse events of special interest related to COVID-19 vaccines in Ontario, Canada, 2015 to 2020, to inform COVID-19 vaccine safety surveillance.

BACKGROUND: Background incidence rates are critical in pharmacovigilance to facilitate identification of vaccine safety signals. We estimated background incidence rates of 11 adverse events of special interest related to COVID-19 vaccines in Ontario, Canada. METHODS: We conducted a population-based retrospective observational study using linked health administrative databases for hospitalizations and emergency department visits among Ontario residents. We estimated incidence rates of Bell's palsy, idiopathic thrombocytopenia, febrile convulsions, acute disseminated encephalomyelitis, myocarditis, pericarditis, Kawasaki disease, Guillain-Barré syndrome, transverse myelitis, acute myocardial infarction, and anaphylaxis during five pre-pandemic years (2015-2019) and 2020. RESULTS: The average annual population was 14 million across all age groups with 51% female. The pre-pandemic mean annual rates per 100,000 population during 2015-2019 were 191 for acute myocardial infarction, 43.9 for idiopathic thrombocytopenia, 28.8 for anaphylaxis, 27.8 for Bell's palsy, 25.0 for febrile convulsions, 22.8 for acute disseminated encephalomyelitis, 11.3 for myocarditis/pericarditis, 8.7 for pericarditis, 2.9 for myocarditis, 2.0 for Kawasaki disease, 1.9 for Guillain-Barré syndrome, and 1.7 for transverse myelitis. Females had higher rates of acute disseminated encephalomyelitis, transverse myelitis and anaphylaxis while males had higher rates of myocarditis, pericarditis, and Guillain-Barré syndrome. Bell's palsy, acute disseminated encephalomyelitis, and Guillain-Barré syndrome increased with age. The mean rates of myocarditis and/or pericarditis increased with age up to 79 years; males had higher rates than females: from 12 to 59 years for myocarditis and ≥12 years for pericarditis. Febrile convulsions and Kawasaki disease were predominantly childhood diseases and generally decreased with age. CONCLUSIONS: Our estimated background rates will permit estimating numbers of expected events for these conditions and facilitate detection of potential safety signals following COVID-19 vaccination.

Changes in incidence rates of outcomes of interest in vaccine safety studies during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic caused an abrupt drop in in-person health care (inpatient, Emergency Department, outpatient) and an increase in telehealth care, which poses challenges in vaccine safety studies that identify outcomes from in-person encounters. We examined the changes in incidence rates of selected encounter-based outcomes during the COVID-19 pandemic. METHODS: We assembled a cohort of members from 8 Vaccine Safety Datalink sites from January 1, 2017 through December 31, 2020. Using ICD-10 diagnosis codes or laboratory criteria, we identified 21 incident outcomes in traditional in-person settings and all settings. We defined 4 periods in 2020: January-February (pre-pandemic), April-June (early pandemic), July-September (middle pandemic), and October-December (late pandemic). We defined four corresponding periods in each year during 2017-2019. We calculated incidence rates, conducted difference in difference (DiD) analyses, and reported ratios of incidence rate ratios (RRR) to examine changes in rates from pre-pandemic to early, middle, and late pandemic in 2020, after adjusting for changes across similar periods in 2017-2019. RESULTS: Among > 10 million members, regardless of setting and after adjusting for changes during 2017-2019, we found that incidence rates of acute disseminated encephalomyelitis, encephalitis/myelitis/encephalomyelitis/meningoencephalitis, and thrombotic thrombocytopenic purpura did not significantly change from the pre-pandemic to early, middle or late pandemic periods (p-values ≥ 0.05). Incidence rates decreased from the pre-pandemic to early pandemic period during 2020 for acute myocardial infarction, anaphylaxis, appendicitis, Bell's palsy, convulsions/seizures, Guillain-Barré syndrome, immune thrombocytopenia (ITP), narcolepsy/cataplexy, hemorrhagic stroke, ischemic stroke, and venous thromboembolism (p-values < 0.05). Incidence rates of Bell's palsy, ITP, and narcolepsy/cataplexy were higher in all settings than in traditional in-person settings during the three pandemic periods (p-values < 0.05). CONCLUSION: Rates of some clinical outcomes during the pandemic changed and should not be used as historical background rates in vaccine safety studies. Inclusion of telehealth visits should be considered for vaccine studies involving Bell's palsy, ITP, and narcolepsy/cataplexy.

Incidence and Clinical Characteristics of and Risk Factors for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection Among Pregnant Individuals in the United States.

BACKGROUND: Data about the risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among pregnant individuals are needed to inform infection-prevention guidance and counseling for this population. METHODS: We prospectively followed a cohort of pregnant individuals during August 2020-March 2021 at 3 US sites. The 3 primary outcomes were incidence rates of any SARS-CoV-2 infection, symptomatic infection, and asymptomatic infection, during pregnancy during periods of SARS-CoV-2 circulation. Participants self-collected weekly midturbinate nasal swabs for SARS-CoV-2 reverse transcription-polymerase chain reaction testing, completed weekly illness symptom questionnaires, and submitted additional swabs with coronavirus disease 2019 (COVID-19)-like symptoms. An overall SARS-CoV-2 infection incidence rate weighted by population counts of women of reproductive age in each state was calculated. RESULTS: Among 1098 pregnant individuals followed for a mean of 10 weeks, 9% (99/1098) had SARS-CoV-2 infections during the study. Population-weighted incidence rates of SARS-CoV-2 infection were 10.0 per 1000 (95% confidence interval, 5.7-14.3) person-weeks for any infection, 5.7 per 1000 (1.7-9.7) for symptomatic infections, and 3.5 per 1000 (0-7.1) for asymptomatic infections. Among 96 participants with SARS-CoV-2 infections and symptom data, the most common symptoms were nasal congestion (72%), cough (64%), headache (59%), and change in taste or smell (54%); 28% had measured or subjective fever. Median symptom duration was 10 (interquartile range, 6-16) days. CONCLUSIONS: Pregnant individuals in this study had a 1% risk of SARS-CoV-2 infection per week, underscoring the importance of COVID-19 vaccination and other prevention measures during pregnancy while SARS-CoV-2 is circulating in the community.

U.S. Population-Based background incidence rates of medical conditions for use in safety assessment of COVID-19 vaccines.

The Coronavirus Disease 2019 (COVID-19) pandemic has had a devastating impact on global health, and has resulted in an unprecedented, international collaborative effort to develop vaccines to control the outbreak, protect human lives, and avoid further social and economic disruption. Mass vaccination campaigns are underway in multiple countries and are expected worldwide once more vaccine becomes available. Some early candidate vaccines use novel platforms, such as mRNA encapsulated in lipid nanoparticles, and relatively new platforms, such as replication-deficient viral vectors. While these new vaccine platforms hold promise, limited safety data in humans are available. Serious health outcomes linked to vaccinations are rare, and some outcomes may occur incidentally in the vaccinated population. Knowledge of background incidence rates of these medical conditions is a critical component of vaccine safety monitoring to aid in the assessment of adverse events temporally associated with vaccination and to put these events into context with what would be expected due to chance alone. A list of 22 potential adverse events of special interest (AESI), including neurologic, autoimmune, and cardiovascular disorders, was compiled by subject matter experts at the U.S. Food and Drug Administration and the Centers for Disease Control and Prevention. The most recently available U.S. background rates for these medical conditions, overall and by age, sex, and race/ethnicity (when available), were sourced from reported statistics (data published by medical panels/ associations or federal government reports), and literature reviews in PubMed. This review provides estimates of background incidence rates for medical conditions that may be monitored or studied as AESI during safety surveillance and research for COVID-19 vaccines and other new vaccines.

[A Proposed Epidemiologic Risk Threshold for SARS-CoV-2 for Assisting Healthcare Decision-Making]. / Sistema de ayuda a la toma de decisiones sanitarias. Propuesta de umbrales de riesgo epidemiológico ante SARS-CoV-2.

INTRODUCTION: The SARS-CoV-2 pandemic is the most important health challenge observed in 100 years, and since its emergence has generated the highest excess of non-war-related deaths in the western world. Since this disease is highly contagious and 33% of cases are asymptomatic, it is crucial to develop methods to predict its course. We developed a predictive model for Covid-19 infection in Spanish provinces. METHODS: We applied main components analysis to epidemiological data for Spanish provinces obtained from the National Centre of Epidemiology, based on the epidemiological curve between 24 February and 8 June 2020. Using this method, we classified provinces according to their epidemiological progress (worst, intermediate, and good). RESULTS: We identified 2 components that explained 99% of variability in the 52 epidemiological curves. The first component can be interpreted as the crude incidence rate trend and the second component as the speed of increase or decrease in the incidence rate during the period analysed. We identified 10 provinces in the group with the worst progress and 17 in the intermediate group. The threshold values for the 7-day incidence rate for an alert 1 (intermediate) were 134 cases/100,000 inhabitants, and 167 for alert 2 (high), respectively, showing a high discriminative power between provinces. CONCLUSIONS: These alert levels might be useful for deciding which measures may affect population mobility and other public health decisions when considering community transmission of SARS-CoV-2 in a given geographical area. This information would also facilitate intercomparison between healthcare areas and Autonomous Communities.

Associations between COVID-19 Incidence Rates and the Exposure to PM2.5 and NO2: A Nationwide Observational Study in Italy.

The COVID-19 outbreak disproportionately affected the elderly and areas with higher population density. Among the multiple factors possibly involved, a role for air pollution has also been hypothesized. This nationwide observational study demonstrated the significant positive relationship between COVID-19 incidence rates and PM2.5 and NO2 levels in Italy, both considering the period 2016-2020 and the months of the epidemic, through univariate regression models, after logarithmic transformation of the variables, as the data were not normally distributed. That relationship was confirmed by a multivariate analysis showing the combined effect of the two pollutants, adjusted for the old-age index and population density. An increase in PM2.5 and NO2 concentrations by one unit (1 µg/m3) corresponded to an increase in incidence rates of 1.56 and 1.24 × 104 people, respectively, taking into account the average levels of air pollutants in the period 2016-2020, and 2.79 and 1.24 × 104 people during March-May 2020. Considering the entire epidemic period (March-October 2020), these increases were 1.05 and 1.01 × 104 people, respectively, and could explain 59% of the variance in COVID-19 incidence rates (R2 = 0.59). This evidence could support the implementation of targeted responses by focusing on areas with low air quality to mitigate the spread of the disease.

Spatiotemporal analysis and hotspots detection of COVID-19 using geographic information system (March and April, 2020).

Understanding the spatial distribution of coronavirus disease 2019 (COVID-19) cases can provide valuable information to anticipate the world outbreaks and in turn improve public health policies. In this study, the cumulative incidence rate (CIR) and cumulative mortality rate (CMR) of all countries affected by the new corona outbreak were calculated at the end of March and April, 2020. Prior to the implementation of hot spot analysis, the spatial autocorrelation results of CIR were obtained. Hot spot analysis and Anselin Local Moran's I indices were then applied to accurately locate high and low-risk clusters of COVID-19 globally. San Marino and Italy revealed the highest CMR by the end of March, though Belgium took the place of Italy as of 30th April. At the end of the research period (by 30th April), the CIR showed obvious spatial clustering. Accordingly, southern, northern and western Europe were detected in the high-high clusters demonstrating an increased risk of COVID-19 in these regions and also the surrounding areas. Countries of northern Africa exhibited a clustering of hot spots, with a confidence level above 95%, even though these areas assigned low CIR values. The hot spots accounted for nearly 70% of CIR. Furthermore, analysis of clusters and outliers demonstrated that these countries are situated in the low-high outlier pattern. Most of the surveyed countries that exhibited clustering of high values (hot spot) with a confidence level of 99% (by 31st March) and 95% (by 30th April) were dedicated higher CIR values. In conclusion, hot spot analysis coupled with Anselin local Moran's I provides a scrupulous and objective approach to determine the locations of statistically significant clusters of COVID-19 cases shedding light on the high-risk districts.

Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China.

Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is critical in effectively preventing and controlling this epidemic. However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research aims to detect the spatio-temporal patterns of the 2019-nCoV epidemic using spatio-temporal analysis methods at the county level in Hubei province. The Mann-Kendall and Pettitt methods were used to identify the temporal trends and abrupt changes in the time series of daily new confirmed cases, respectively. The local Moran's I index was applied to uncover the spatial patterns of the incidence rate, including spatial clusters and outliers. On the basis of the data from January 26 to February 11, 2020, we found that there were 11 areas with different types of temporal patterns of daily new confirmed cases. The pattern characterized by an increasing trend and abrupt change is mainly attributed to the improvement in the ability to diagnose the disease. Spatial clusters with high incidence rates during the period were concentrated in Wuhan Metropolitan Area due to the high intensity of spatial interaction of the population. Therefore, enhancing the ability to diagnose the disease and controlling the movement of the population can be confirmed as effective measures to prevent and control the regional outbreak of the epidemic.