Influence of population mobility on the novel coronavirus disease (COVID-19) epidemic: based on panel data from Hubei, China

Does a new case-based payment system promote the construction of the ordered health delivery system? Evidence from a pilot city in China

BACKGROUND

The construction of the ordered health delivery system in China aims to enhance equity and optimize the efficient use of medical resources by rationally allocating patients to different levels of medical institutions based on the severity of their condition. However, superior hospitals have been overcrowded, and primary healthcare facilities have been underutilized in recent years. China has developed a new case-based payment method called "Diagnostic Intervention Package" (DIP). The government is trying to use this economic lever to encourage medical institutions to actively assume treatment tasks consistent with their functional positioning and service capabilities.

METHODS

This study takes Tai'an, a DIP pilot city, as a case study and uses an interrupted time series analysis to analyze the impact of DIP reform on the case severity and service scope of medical institutions at different levels.

RESULTS

The results show that after the DIP reform, the proportion of patients receiving complicated procedures (tertiary hospitals: β3 = 0.197, P < 0.001; secondary hospitals: β3 = 0.132, P = 0.020) and the case mix index (tertiary hospitals: β3 = 0.022, P < 0.001; secondary hospitals: β3 = 0.008, P < 0.001) in tertiary and secondary hospitals increased, and the proportion of primary-DIP-groups cases decreased (tertiary hospitals: β3 = -0.290, P < 0.001; secondary hospitals: β3 = -1.200, P < 0.001), aligning with the anticipated policy objectives. However, the proportion of patients receiving complicated procedures (β3 = 0.186, P = 0.002) and the case mix index (β3 = 0.002, P < 0.001) in primary healthcare facilities increased after the reform, while the proportion of primary-DIP-groups cases (β3 = -0.515, P = 0.005) and primary-DIP-groups coverage (β3 = -2.011, P < 0.001) decreased, which will reduce the utilization efficiency of medical resources and increase inequity.

CONCLUSION

The DIP reform did not effectively promote the construction of the ordered health delivery system. Policymakers need to adjust economic incentives and implement restraint mechanisms to regulate the behavior of medical institutions.

Direct indicators of social distancing effectiveness in COVID-19 outbreak stages: a correlational analysis of case contacts and population mobility in Korea

OBJECTIVES

The effectiveness of social distancing during the coronavirus disease 2019 (COVID-19) pandemic has been evaluated using the magnitude of changes in population mobility. This study aimed to investigate a direct indicator-namely, the number of close contacts per patient with confirmed COVID-19.

METHODS

From week 7, 2020 to week 43, 2021, population movement changes were calculated from the data of two Korean telecommunication companies and Google in accordance with social distancing stringency levels. Data on confirmed cases and their close contacts among residents of Gyeonggi Province, Korea were combined at each stage. Pearson correlation analysis was conducted to compare the movement data with the change in the number of contacts for each confirmed case calculated by stratification according to age group. The reference value of the population movement data was set using the value before mid-February 2020, considering each data's characteristics.

RESULTS

In the age group of 18 or younger, the number of close contacts per confirmed case decreased or increased when the stringency level was strengthened or relaxed, respectively. In adults, the correlation was relatively low, with no correlation between the change in the number of close contacts per confirmed case and the change in population movement after the commencement of vaccination for adults.

CONCLUSIONS

The effectiveness of governmental social distancing policies against COVID-19 can be evaluated using the number of close contacts per confirmed case as a direct indicator, especially for each age group. Such an analysis can facilitate policy changes for specific groups.

Epidemic Diffusion Network of Spain: A Mobility Model to Characterize the Transmission Routes of Disease

Human mobility drives the geographical diffusion of infectious diseases at different scales, but few studies focus on mobility itself. Using publicly available data from Spain, we define a Mobility Matrix that captures constant flows between provinces by using a distance-like measure of effective distance to build a network model with the 52 provinces and 135 relevant edges. Madrid, Valladolid and Araba/Álaba are the most relevant nodes in terms of degree and strength. The shortest routes (most likely path between two points) between all provinces are calculated. A total of 7 mobility communities were found with a modularity of 63%, and a relationship was established with a cumulative incidence of COVID-19 in 14 days (CI14) during the study period. In conclusion, mobility patterns in Spain are governed by a small number of high-flow connections that remain constant in time and seem unaffected by seasonality or restrictions. Most of the travels happen within communities that do not completely represent political borders, and a wave-like spreading pattern with occasional long-distance jumps (small-world properties) can be identified. This information can be incorporated into preparedness and response plans targeting locations that are at risk of contagion preventively, underscoring the importance of coordination between administrations when addressing health emergencies.

COVID-19 Related Early Google Search Behavior and Health Communication in the United States: Panel Data Analysis on Health Measures

The COVID-19 outbreak at the end of December 2019 spread rapidly all around the world. The objective of this study is to investigate and understand the relationship between public health measures and the development of the pandemic through Google search behaviors in the United States. Our collected data includes Google search queries related to COVID-19 from 1 January to 4 April 2020. After using unit root tests (ADF test and PP test) to examine the stationary and a Hausman test to choose a random effect model, a panel data analysis is conducted to investigate the key query terms with the newly added cases. In addition, a full sample regression and two sub-sample regressions are proposed to explain: (1) The changes in COVID-19 cases number are partly related to search variables related to treatments and medical resources, such as ventilators, hospitals, and masks, which correlate positively with the number of new cases. In contrast, regarding public health measures, social distancing, lockdown, stay-at-home, and self-isolation measures were negatively associated with the number of new cases in the US. (2) In mild states, which ranked one to twenty by the average daily new cases from least to most in 50 states, the query terms about public health measures (quarantine, lockdown, and self-isolation) have a significant negative correlation with the number of new cases. However, only the query terms about lockdown and self-isolation are also negatively associated with the number of new cases in serious states (states ranking 31 to 50). Furthermore, public health measures taken by the government during the COVID-19 outbreak are closely related to the situation of controlling the pandemic.

High altitude Relieves transmission risks of COVID-19 through meteorological and environmental factors: Evidence from China

Existing studies reported higher altitudes reduce the COVID-19 infection rate in the United States, Colombia, and Peru. However, the underlying reasons for this phenomenon remain unclear. In this study, regression analysis and mediating effect model were used in a combination to explore the altitudes relation with the pattern of transmission under their correlation factors. The preliminary linear regression analysis indicated a negative correlation between altitudes and COVID-19 infection in China. In contrast to environmental factors from low-altitude regions (<1500 m), high-altitude regions (>1500 m) exhibited lower PM2.5, average temperature (AT), and mobility, accompanied by high SO2 and absolute humidity (AH). Non-linear regression analysis further revealed that COVID-19 confirmed cases had a positive correlation with mobility, AH, and AT, whereas negatively correlated with SO2, CO, and DTR. Subsequent mediating effect model with altitude-correlated factors, such as mobility, AT, AH, DTR and SO2, suffice to discriminate the COVID-19 infection rate between low- and high-altitude regions. The mentioned evidence advance our understanding of the altitude-mediated COVID-19 transmission mechanism.

Agent Simulation Model of COVID-19 Epidemic Agent-Based on GIS: A Case Study of Huangpu District, Shanghai

Since the COVID-19 outbreak was detected and reported at the end of 2019, the pandemic continues worldwide, with public health authorities and the general public in each country struggling to balance safety and normal travel activities. However, the complex public health environment and the complexity of human behaviors, as well as the constant mutation of the COVID-19 virus, requires the development of theoretical and simulation tools to accurately model all segments of society. In this paper, an agent-based model is proposed, the model constructs the real geographical environment of Shanghai Huangpu District based on the building statistics data of Shanghai Huangpu District, and the real population data of Shanghai Huangpu District based on the data of China's seventh Population census in 2020. After incorporating the detailed elements of COVID-19 transmission and the real data of WHO, the model forms various impact parameters. Finally, the model was validated according to the COVID-19 data reported by the official, and the model is applied to a hypothetical scenario. Shanghai is one of the places hardest hit by the current outbreak, Huangpu District is the "heart, window and name card" of Shanghai, and its importance to Shanghai is self-evident. so we used one-to-one population modeling to simulate the spread of COVID-19 in Huangpu District of Shanghai, In addition to the conventional functions of crowd movement, detection and treatment, the model also takes into account the burden of nucleic acid detection on the model caused by diseases similar to COVID-19, such as seasonal cold. The model validation results show that we have constructed a COVID-19 epidemic agent risk assessment system suitable for the individual epidemiological characteristics of COVID-19 in China, which can adjust and reflect on the existing COVID-19 epidemic intervention strategies and individual health behaviors. To provide scientific theoretical basis and information decision-making tools for effective prevention and control of COVID-19 and public health intervention in China.

A community-level study on COVID-19 transmission and policy interventions in Wuhan, China

The specific factors and response strategies that affect COVID-19 transmission in local communities remain under-explored in the current literature due to a lack of data. Based on primary COVID-19 data collected at the community level in Wuhan, China, our study contributes a community-level investigation on COVID-19 transmission and response strategies by addressing two research questions: 1) What community factors are associated with viral transmission? and 2) What are the key mechanisms behind policy interventions towards controlling viral transmission within local communities? We conducted two sets of analyses to address these two questions-quantitative analyses of the relationship between community factors and viral transmission and qualitative analyses of policy interventions on community transmission. Our findings show that the viral spread in local communities is irrelevant to the built environment of a community and its socioeconomic position but is related to its demographic composition. Specifically, groups under the age of 18 play an important role in viral transmission. Moreover, a series of community shutdown management initiatives (e.g., group buying, delivering supplies, and self-reporting of health conditions) play an important role in curbing viral transmission at the local level that can be applied to other geographic contexts.

Social Network Analysis on the Mobility of Three Vulnerable Population Subgroups: Domestic Workers, Flight Crews, and Sailors during the COVID-19 Pandemic in Hong Kong

BACKGROUND

Domestic workers, flight crews, and sailors are three vulnerable population subgroups who were required to travel due to occupational demand in Hong Kong during the COVID-19 pandemic.

OBJECTIVE

The aim of this study was to explore the social networks among three vulnerable population subgroups and capture temporal changes in their probability of being exposed to SARS-CoV-2 via mobility.

METHODS

We included 652 COVID-19 cases and utilized Exponential Random Graph Models to build six social networks: one for the cross-sectional cohort, and five for the temporal wave cohorts, respectively. Vertices were the three vulnerable population subgroups. Edges were shared scenarios where vertices were exposed to SARS-CoV-2.

RESULTS

The probability of being exposed to a COVID-19 case in Hong Kong among the three vulnerable population subgroups increased from 3.38% in early 2020 to 5.78% in early 2022. While domestic workers were less mobile intercontinentally compared to flight crews and sailors, domestic workers were 1.81-times in general more likely to be exposed to SARS-CoV-2.

CONCLUSIONS

Vulnerable populations with similar ages and occupations, especially younger domestic workers and flight crew members, were more likely to be exposed to SARS-CoV-2. Social network analysis can be used to provide critical information on the health risks of infectious diseases to vulnerable populations.

Associations between COVID-19 Pandemic, Lockdown Measures and Human Mobility: Longitudinal Evidence from 86 Countries

Recognizing an urgent need to understand the dynamics of the pandemic's severity, this longitudinal study is conducted to explore the evolution of complex relationships between the COVID-19 pandemic, lockdown measures, and social distancing patterns in a diverse set of 86 countries. Collecting data from multiple sources, a structural equation modeling (SEM) technique is applied to understand the interdependencies between independent variables, mediators, and dependent variables. Results show that lockdown and confinement measures are very effective to reduce human mobility at retail and recreation facilities, transit stations, and workplaces and encourage people to stay home and thereby control COVID-19 transmission at critical times. The study also found that national contexts rooted in socioeconomic and institutional factors influence social distancing patterns and severity of the pandemic, particularly with regard to the vulnerability of people, treatment costs, level of globalization, employment distribution, and degree of independence in society. Additionally, this study portrayed a mutual relationship between the COVID-19 pandemic and human mobility. A higher number of COVID-19 confirmed cases and deaths reduces human mobility and the countries with reduced personal mobility have experienced a deepening of the severity of the pandemic. However, the effect of mobility on pandemic severity is stronger than the effect of pandemic situations on mobility. Overall, the study displays considerable temporal changes in the relationships between independent variables, mediators, and dependent variables considering pandemic situations and lockdown regimes, which provides a critical knowledge base for future handling of pandemics. It has also accommodated some policy guidelines for the authority to control the transmission of COVID-19.

Phased repatriation of Lebanese expatriates stranded abroad during coronavirus disease 2019 (COVID-19) pandemic

Background

The coronavirus disease (COVID-19) pandemic represents a serious worldwide threat. Stranded Lebanese citizens abroad appealed to the Lebanese government to embark on citizen repatriation missions. We aim to document the Lebanese experience in the repatriation of citizens during COVID-19 which allow us to disclose encountered challenges and lessons learned.

Methods

This is a retrospective description of processes involved in the phased repatriation of Lebanese citizens. The Mission consisted of 4 phases starting, April 5th until June 19th 2020. The prioritization of returnees was based on both medical and social risk assessment. The repatriation team was divided into four groups: the aircraft team, the airport team, the hotel team and the follow up team. On arrival, all returning citizens were tested using Polymerase chain Reaction (PCR) based technique, and were obliged to adhere to a mandatory facility quarantine for 24 to 48 h. Returning travelers who were tested positive for COVID-19 were transferred to the hospital. Those who were tested negative were urged to strictly comply with home-quarantine for a duration of 14 days. They were followed up on a daily basis by the repatriation team.

Results

Overall, 25,783 Lebanese citizens have returned home during the phased repatriation. The third phase ranked the uppermost in regard of the number of citizens repatriated. The total number of performed PCR tests at the airport upon arrival was 14,893 with an average percentage of around 1% positivity for COVID-19. On the other hand, more than 10,687 repatriates underwent external PCR requisite in the third and fourth phases. Two hundred seventy-two repatriates were tested positive for COVID-19 upon their arrival.

Conclusion

Considering the limited human and financial resources besides the economic and political crisis, the overall repatriation mission could be considered as a successful experience. Such processes would not have been achieved without the professionalism of all involved stakeholders.

The Impact of Mobility Restriction Strategies in the Control of the COVID-19 Pandemic: Modelling the Relation between COVID-19 Health and Community Mobility Data

BACKGROUND

Non-pharmaceutical interventions (NPIs), particularly mobility restrictions, are mainstay measures for the COVID-19 pandemic worldwide. We evaluated the effects of Oman's mobility restriction strategies to highlight their efficacy in controlling the pandemic.

METHODS

Accessible national data of daily admissions and deaths were collected from 1 April 2020 to 22 May 2021. Google Community Mobility Report (CMR) data were downloaded for the same period. Among six CMR categories, three were used and reduced to one index-the community mobility index (CMI). We used a generalised linear model with a negative binomial distribution combined with a non-linear distributed lag model to investigate the short-term effects of CMI on the number of admitted PCR-confirmed COVID-19 cases and deaths, controlling for public holidays, day of the week, and Eid/Ramadan days.

RESULTS

We demonstrated the feasibility of using CMRs in the evaluation and monitoring of different NPIs, particularly those related to movement restriction. The best movement restriction strategy was a curfew from 7 p.m. to 5 a.m. (level 3 of CMI = 8), which had a total reduction of 35% (95% confidence interval (CI); 25-44%) in new COVID-19 admissions in the following two weeks, and a fatality reduction in the following four weeks by 52% (95% CI; 11-75%).

CONCLUSION

Evening lockdown significantly affected the course of the pandemic in Oman which lines up with similar studies throughout the world.

Spatial association of mobility and COVID-19 infection rate in the USA: A county-level study using mobile phone location data

INTRODUCTION

Human mobility has been a central issue in the discussion from the beginning of COVID-19. While the body of literature on the relationship of COVID transmission and mobility is large, studies mostly captured a relatively short timeframe. Moreover, spatial non-stationarity has garnered less attention in these explorative models. Therefore, the major concern of this study is to see the relationship of mobility and COVID on a broader temporal scale and after mitigating this methodological gap.

OBJECTIVE

In response to this concern, this study first explores the spatiotemporal pattern of mobility indicators. Secondly, it attempts to understand how mobility is related to COVID infection rate and how this relationship has been changed over time and space after controlling several sociodemographic characteristics, spatial heterogeneity, and policy-related changes during different phases of Coronavirus.

DATA AND METHOD

This study uses GPS-based mobility data for a wider time frame of six months (March 20-August'20) divided into four tiers and carries analysis for all the US counties (N = 3142). Space-time cube is used to generate the spatiotemporal pattern. For the second objective, Ordinary Least Square (OLS), Spatial Error Model (SEM), and Geographically Weighted Regression (GWR) were used.

RESULT

The spatial-temporal pattern suggests that the trip rate, out-of-county trip rate, and miles/person traveled were mostly plummeted till the first wave reached its peak, and subsequently, all of these mobility matrices started to rise. From spatial models, infection rates were found negatively correlated with miles traveled and out-of-county trips. Highly COVID infected areas mostly had more people working from home, low percentages of aged people and educated people, and high percentages of poor people.

CONCLUSION

This study, with necessary policy implications, provides a comprehensive understanding of the shifting pattern of mobility and COVID. Spatial models outperform OLS with better fits and non-clustered residuals.

Spatio-temporal characteristics and control strategies in the early period of COVID-19 spread: a case study of the mainland China

COVID-19 has caused huge impacts on human health and the economic operation of the world. Analyzing and summarizing the early propagation law can help reduce the losses caused by public health emergencies in the future. Early data on the spread of COVID-19 in 30 provinces (autonomous regions and municipalities) of mainland China except for Hubei, Hong Kong, Macao, and Taiwan were selected in this study. Spatio-temporal analysis, inflection point analysis, and correlation analysis are used to explore the spatio-temporal characteristics in the early COVID-19 spread. The results suggested that (1) the total confirmed cases have risen in an "S"-shaped curve over time, and the daily new cases have first increased and finally decreased; (2) the spatial distributions of both total and daily new cases show a trend of more in the east and less in the west, with a "multi-center agglomeration distribution" around Hubei Province and some major cities; (3) the spatial agglomeration of total confirmed cases has been increasing over time, while that of the daily new cases shows much more obvious in the mid-stage; and (4) timely release of the first-level public health emergency response can accelerate the emergence of the epidemic inflection point. The above analysis results have a specific reference value for the government's policy-making and measures to face public health emergencies.

Bayesian spatiotemporal forecasting and mapping of COVID-19 risk with application to West Java Province, Indonesia

The coronavirus disease (COVID-19) has spread rapidly to multiple countries including Indonesia. Mapping its spatiotemporal pattern and forecasting (small area) outbreaks are crucial for containment and mitigation strategies. Hence, we introduce a parsimonious space-time model of new infections that yields accurate forecasts but only requires information regarding the number of incidences and population size per geographical unit and time period. Model parsimony is important because of limited knowledge regarding the causes of COVID-19 and the need for rapid action to control outbreaks. We outline the basics of Bayesian estimation, forecasting, and mapping, in particular for the identification of hotspots. The methodology is applied to county-level data of West Java Province, Indonesia.

A Short-Term Prediction Model at the Early Stage of the COVID-19 Pandemic Based on Multisource Urban Data

The ongoing coronavirus disease 2019 (COVID-19) pandemic spread throughout China and worldwide since it was reported in Wuhan city, China in December 2019. 4 589 526 confirmed cases have been caused by the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), by May 18, 2020. At the early stage of the pandemic, the large-scale mobility of humans accelerated the spread of the pandemic. Rapidly and accurately tracking the population inflow from Wuhan and other cities in Hubei province is especially critical to assess the potential for sustained pandemic transmission in new areas. In this study, we first analyze the impact of related multisource urban data (such as local temperature, relative humidity, air quality, and inflow rate from Hubei province) on daily new confirmed cases at the early stage of the local pandemic transmission. The results show that the early trend of COVID-19 can be explained well by human mobility from Hubei province around the Chinese Lunar New Year. Different from the commonly-used pandemic models based on transmission dynamics, we propose a simple but effective short-term prediction model for COVID-19 cases, considering the human mobility from Hubei province to the target cities. The performance of our proposed model is validated by several major cities in Guangdong province. For cities like Shenzhen and Guangzhou with frequent population flow per day, the values of [Formula: see text] of daily prediction achieve 0.988 and 0.985. The proposed model has provided a reference for decision support of pandemic prevention and control in Shenzhen.

Effects of Wise Intervention on Perceived Discrimination Among College Students Returning Home From Wuhan During the COVID-19 Outbreak

At the beginning of the coronavirus disease 2019 (COVID-19) outbreak, college students returning home from Wuhan, Hubei Province, experienced various degrees of discrimination. This study first investigates perceived discrimination among college students returning home from Wuhan. Then, an experimental method is used to investigate the effectiveness of an intervention designed to reduce the perceived discrimination among those who returned to towns outside of Hubei Province. A total of 63 college students participated in the experiment. In the experimental group (N = 31), a wise intervention based on reading and writing was adopted to intervene in perceived discrimination among the participants. The results showed that the perceived discrimination among students returning from Wuhan to towns outside of Hubei Province was significantly higher than that among students returning to towns within Hubei Province. The wise intervention reduced the perceived discrimination in the experimental group but not in the control group. Further analysis found that perceived social support fully mediated the relationship between the intervention and perceived discrimination. These results provide insights on how the content of intervention (perceived social support) and the form of intervention (wise intervention) can prevent the occurrence of psychological problems in epidemic situations.

The comparison of epidemiological characteristics between confirmed and clinically diagnosed cases with COVID-19 during the early epidemic in Wuhan, China

BACKGROUND

To put COVID-19 patients into hospital timely, the clinical diagnosis had been implemented in Wuhan in the early epidemic. Here we compared the epidemiological characteristics of laboratory-confirmed and clinically diagnosed cases with COVID-19 in Wuhan.

METHODS

Demographics, case severity and outcomes of 29,886 confirmed cases and 21,960 clinically diagnosed cases reported between December 2019 and February 24, 2020, were compared. The risk factors were estimated, and the effective reproduction number (Rt) of SARS-CoV-2 was also calculated.

RESULTS

The age and occupation distribution of confirmed cases and clinically diagnosed cases were consistent, and their sex ratio were 1.0 and 0.9, respectively. The epidemic curve of clinical diagnosis cases was similar to that of confirmed cases, and the city centers had more cumulative cases and higher incidence density than suburbs in both of two groups. The proportion of severe and critical cases (21.5 % vs. 14.0 %, P < 0.0001) and case fatality rates (5.2 % vs. 1.2 %, P < 0.0001) of confirmed cases were all higher than those of clinically diagnosed cases. Risk factors for death we observed in both of two groups were older age, male, severe or critical cases. Rt showed the same trend in two groups, it dropped below 1.0 on February 6 among confirmed cases, and February 8 among clinically diagnosed cases.

CONCLUSIONS

The demographic characteristics and spatiotemporal distributions of confirmed and clinically diagnosed cases are roughly similar, but the disease severity and clinical outcome of clinically diagnosed cases are better than those of confirmed cases. In cases when detection kits are insufficient during the early epidemic, the implementation of clinical diagnosis is necessary and effective.

A predictive internet-based model for COVID-19 hospitalization census

The COVID-19 pandemic has strained hospital resources and necessitated the need for predictive models to forecast patient care demands in order to allow for adequate staffing and resource allocation. Recently, other studies have looked at associations between Google Trends data and the number of COVID-19 cases. Expanding on this approach, we propose a vector error correction model (VECM) for the number of COVID-19 patients in a healthcare system (Census) that incorporates Google search term activity and healthcare chatbot scores. The VECM provided a good fit to Census and very good forecasting performance as assessed by hypothesis tests and mean absolute percentage prediction error. Although our study and model have limitations, we have conducted a broad and insightful search for candidate Internet variables and employed rigorous statistical methods. We have demonstrated the VECM can potentially be a valuable component to a COVID-19 surveillance program in a healthcare system.

Comparison of Predictive Models and Impact Assessment of Lockdown for COVID-19 over the United States

The novel Coronavirus Disease 2019 (COVID-19) remains a worldwide threat to community health, social stability, and economic development. Since the first case was recorded on December 29, 2019, in Wuhan of China, the disease has rapidly extended to other nations of the world to claim many lives, especially in the USA, the United Kingdom, and Western Europe. To stay ahead of the curve consequent of the continued increase in case and mortality, predictive tools are needed to guide adequate response. Therefore, this study aims to determine the best predictive models and investigate the impact of lockdown policy on the USA’ COVID-19 incidence and mortality. This study focuses on the statistical modelling of the USA daily COVID-19 incidence and mortality cases based on some intuitive properties of the data such as overdispersion and autoregressive conditional heteroscedasticity. The impact of the lockdown policy on cases and mortality was assessed by comparing the USA incidence case with that of Sweden where there is no strict lockdown. Stochastic models based on negative binomial autoregressive conditional heteroscedasticity [NB INGARCH (p,q)], the negative binomial regression, the autoregressive integrated moving average model with exogenous variables (ARIMAX) and without exogenous variables (ARIMA) models of several orders are presented, to identify the best fitting model for the USA daily incidence cases. The performance of the optimal NB INGARCH model on daily incidence cases was compared with the optimal ARIMA model in terms of their Akaike Information Criteria (AIC). Also, the NB model, ARIMA model and without exogenous variables are formulated for USA daily COVID-19 death cases. It was observed that the incidence and mortality cases show statistically significant increasing trends over the study period. The USA daily COVID-19 incidence is autocorrelated, linear and contains a structural break but exhibits autoregressive conditional heteroscedasticity. Observed data are compared with the fitted data from the optimal models. The results further indicate that the NB INGARCH fits the observed incidence better than ARIMA while the NB models perform better than the optimal ARIMA and ARIMAX models for death counts in terms of AIC and root mean square error (RMSE). The results show a statistically significant relationship between the lockdown policy in the USA and incidence and death counts. This suggests the efficacy of the lockdown policy in the USA.

Cross-national variations in COVID-19 outbreak in West Africa: Where does Nigeria stand in the pandemic?

Nigeria is currently the worst COVID-19 affected country in West Africa in terms of morbidity and mortality amid ECOWAS’s recent proclamation of the country as the region’s COVID-19 Response Champion. It is against this background that this paper analysed the geographical distribution of confirmed COVID-19 cases and fatalities in West Africa, with a view to understanding why Nigeria is at the heart of the pandemic in the sub-continent. The research relied on COVID-19 data and other health, demographic, transport, economic indicators from published sources. Pearson correlation technique and simple linear regressions were useful in discerning associations between COVID-19 and explanatory factors in West Africa. In order of importance, Nigeria, Ghana and Senegal were the top three on the morbidity list while Nigeria, Mali and Niger had the largest number of fatalities as at June 11, 2020. Results show that the population size and air traffic had significant impact on both COVID-19 morbidity and mortality in West Africa. In addition, Nigeria’s large population size and high air traffic volume did not only increase its susceptibility to the viral infection but also accounted for its being an outlier in the sub-continent. The study recommends that a cautious and gradual reopening of the borders should be considered by member states of the sub-region while behavioural avoidance measures are being enforced till a vaccine is found.

The Coupled Impact of Emergency Responses and Population Flows on the COVID-19 Pandemic in China

Coronavirus disease 2019 (COVID-19) has spread around the world and requires effective control measures. Like the human-to-human transmission of the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), the distribution of COVID-19 was driven by population flow and required emergency response measures to slow down its spread and degrade the epidemic risk. The local epidemic risk of COVID-19 is a combination of emergency response measures and population flow. Because of the spatial heterogeneity, the different impacts of coupled emergency responses and population flow on the COVID-19 epidemic during the outbreak period and a control period are unclear. We examined and compared the impact of emergency response measures and population flow on China's epidemic risk after the Wuhan lockdown during the outbreak period and a control period. We found that the population flow out of Wuhan had a long-term impact on the epidemic's spread. In the outbreak period, a large population flow out of Wuhan led to nationwide migration mobility, which directly increased the epidemic in each province. Meanwhile, quick emergency responses mitigated the spread. Although low population flow to provinces far from Hubei delayed the outbreak in those provinces, relatively delayed emergency response increased the epidemic in the control period. Consequently, due to the strong transmission ability of the SARS-CoV-2 virus, no region correctly estimated the epidemic, and the relaxed emergency response raised the epidemic risks in the context of the outbreak.

SARS-CoV-2 in Argentina: Lockdown, mobility, and contagion

There is a debate in Argentina about the effectiveness of mandatory lockdown policies containing severe acute respiratory syndrome coronavirus type 2 disease. This policy has already 6 months long making it one of the longest in the world. The population effort to comply with the lockdown has been decreasing over time given the economic and social costs that it entails. This contribution analyzes the relationship between mobility and contagion in Argentina at a provincial level. It also models issues of internal political discussion on regional contagion and the effect of protests and unexpected crowd events. I use pool, fixed, and random effects panel data modeling and results show that lockdown in Argentina has been effective in reducing mobility but not in a way that reduces the rate of contagion. Strict lockdown seems to be effective in short periods of time and but extend it without complementary mitigation measures it losses effectiveness. The contagion rate seems to be discretely displaced in time and resurges amidst slowly increasing in mobility.

Correction to: Influence of population mobility on the novel coronavirus disease (COVID-19) epidemic: based on panel data from Hubei, China

[This corrects the article DOI: 10.1186/s41256-020-00151-6.].