A municipality-level analysis of excess mortality in Italy in the period January-April 2020.

BACKGROUND: the first confirmed cases of COVID-19 in WHO European Region was reported at the end of January 2020 and, from that moment, the epidemic has been speeding up and rapidly spreading across Europe. The health, social, and economic consequences of the pandemic are difficult to evaluate, since there are many scientific uncertainties and unknowns. OBJECTIVES: the main focus of this paper is on statistical methods for profiling municipalities by excess mortality, directly or indirectly caused by COVID-19. METHODS: the use of excess mortality for all causes has been advocated as a measure of impact less vulnerable to biases. In this paper, observed mortality for all causes at municipality level in Italy in the period January-April 2020 was compared to the mortality observed in the corresponding period in the previous 5 years (2015-2019). Mortality data were made available by the Ministry of Internal Affairs Italian National Resident Population Demographic Archive and the Italian National Institute of Statistics (Istat). For each municipality, the posterior predictive distribution under a hierarchical null model was obtained. From the posterior predictive distribution, we obtained excess death counts, attributable community rates and q-values. Full Bayesian models implemented via MCMC simulations were used. RESULTS: absolute number of excess deaths highlights the burden paid by major cities to the pandemic. The Attributable Community Rate provides a detailed picture of the spread of the pandemic among the municipalities of Lombardy, Piedmont, and Emilia-Romagna Regions. Using Q-values, it is clearly recognizable evidence of an excess of mortality from late February to April 2020 in a very geographically scattered number of municipalities. A trade-off between false discoveries and false non-discoveries shows the different values of public health actions. CONCLUSIONS: despite the variety of approaches to calculate excess mortality, this study provides an original methodological approach to profile municipalities with excess deaths accounting for spatial and temporal uncertainty.

Early analysis of the Australian COVID-19 epidemic.

As of 1 May 2020, there had been 6808 confirmed cases of COVID-19 in Australia. Of these, 98 had died from the disease. The epidemic had been in decline since mid-March, with 308 cases confirmed nationally since 14 April. This suggests that the collective actions of the Australian public and government authorities in response to COVID-19 were sufficiently early and assiduous to avert a public health crisis - for now. Analysing factors that contribute to individual country experiences of COVID-19, such as the intensity and timing of public health interventions, will assist in the next stage of response planning globally. We describe how the epidemic and public health response unfolded in Australia up to 13 April. We estimate that the effective reproduction number was likely below one in each Australian state since mid-March and forecast that clinical demand would remain below capacity thresholds over the forecast period (from mid-to-late April).

Geographical patterns of social cohesion drive disparities in early COVID infection hazard.

The uneven spread of COVID-19 has resulted in disparate experiences for marginalized populations in urban centers. Using computational models, we examine the effects of local cohesion on COVID-19 spread in social contact networks for the city of San Francisco, finding that more early COVID-19 infections occur in areas with strong local cohesion. This spatially correlated process tends to affect Black and Hispanic communities more than their non-Hispanic White counterparts. Local social cohesion thus acts as a potential source of hidden risk for COVID-19 infection.

The Lyme and Tickborne Disease Dashboard: A map-based resource to promote public health awareness and research collaboration.

With the incidence of Lyme and other tickborne diseases on the rise in the US and globally, there is a critical need for data-driven tools that communicate the magnitude of this problem and help guide public health responses. We present the Johns Hopkins Lyme and Tickborne Disease Dashboard (https://www.hopkinslymetracker.org/), a new tool that harnesses the power of geography to raise awareness and fuel research and scientific collaboration. The dashboard is unique in applying a geographic lens to tickborne diseases, aiming not only to become a global tracker of tickborne diseases but also to contextualize their complicated geography with a comprehensive set of maps and spatial data sets representing a One Health approach. We share our experience designing and implementing the dashboard, describe the main features, and discuss current limitations and future directions.

Prevalence of SARS-CoV-2 antibodies in hemodialysis patients in Senegal: a multicenter cross-sectional study.

BACKGROUND: Hemodialysis patients are among high-risk groups for COVID-19. Africa is the continent with the lowest number of cases in the general population but we have little information about the disease burden in dialysis patients. OBJECTIVES: This study aimed to describe the seroprevalence of SARS-CoV-2 antibodies in the hemodialysis population of Senegal. PATIENTS AND METHODS: We conducted a multicenter cross-sectional survey, between June and September 2020 involving 10 public dialysis units randomly selected in eight regions of Senegal. After seeking their consent, we included 303 patients aged ≥ 18 years and hemodialysis for ≥ 3 months. Clinical symptoms and biological parameters were collected from medical records. Patients' blood samples were tested with Abbott SARS-CoV-2 Ig G assay using an Architect system. Statistical tests were performed with STATA 12.0. RESULTS: Seroprevalence of SARS-CoV-2 antibodies was 21.1% (95% CI = 16.7-26.1%). We noticed a wide variability in SARS-CoV-2 seroprevalence between regions ranging from 5.6 to 51.7%. Among the 38 patients who underwent nasal swab testing, only six had a PCR-confirmed infection and all of them did seroconvert. Suggestive clinical symptoms were reported by 28.1% of seropositive patients and the majority of them presented asymptomatic disease. After multivariate analysis, a previous contact with a confirmed case and living in a high population density region were associated with the presence of SARS-CoV-2 antibodies. CONCLUSION: This study presents to our knowledge the first seroprevalence data in African hemodialysis patients. Compared to data from other continents, we found a higher proportion of patients with SARS-CoV-2 antibodies but a lower lethality rate.

COVID-19: Mechanistic Model of the African Paradox Supports the Central Role of the NF-κB Pathway.

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has expanded into a global pandemic, with more than 220 million affected persons and almost 4.6 million deaths by 8 September 2021. In particular, Europe and the Americas have been heavily affected by high infection and death rates. In contrast, much lower infection rates and mortality have been reported generally in Africa, particularly in the sub-Saharan region (with the exception of the Southern Africa region). There are different hypotheses for this African paradox, including less testing, the young age of the population, genetic disposition, and behavioral and epidemiological factors. In the present review, we address different immunological factors and their correlation with genetic factors, pre-existing immune status, and differences in cytokine induction patterns. We also focus on epidemiological factors, such as specific medication coverage, helminth distribution, and malaria endemics in the sub-Saharan region. An analysis combining different factors is presented that highlights the central role of the NF-κB signaling pathway in the African paradox. Importantly, insights into the interplay of different factors with the underlying immune pathological mechanisms for COVID-19 can provide a better understanding of the disease and the development of new targets for more efficient treatment strategies.

Clinical Characterization and Genomic Analysis of Samples from COVID-19 Breakthrough Infections during the Second Wave among the Various States of India.

From March to June 2021, India experienced a deadly second wave of COVID-19, with an increased number of post-vaccination breakthrough infections reported across the country. To understand the possible reason for these breakthroughs, we collected 677 clinical samples (throat swab/nasal swabs) of individuals from 17 states/Union Territories of the country who had received two doses (n = 592) and one dose (n = 85) of vaccines and tested positive for COVID-19. These cases were telephonically interviewed and clinical data were analyzed. A total of 511 SARS-CoV-2 genomes were recovered with genome coverage of higher than 98% from both groups. Analysis of both groups determined that 86.69% (n = 443) of them belonged to the Delta variant, along with Alpha, Kappa, Delta AY.1, and Delta AY.2. The Delta variant clustered into four distinct sub-lineages. Sub-lineage I had mutations in ORF1ab A1306S, P2046L, P2287S, V2930L, T3255I, T3446A, G5063S, P5401L, and A6319V, and in N G215C; Sub-lineage II had mutations in ORF1ab P309L, A3209V, V3718A, G5063S, P5401L, and ORF7a L116F; Sub-lineage III had mutations in ORF1ab A3209V, V3718A, T3750I, G5063S, and P5401L and in spike A222V; Sub-lineage IV had mutations in ORF1ab P309L, D2980N, and F3138S and spike K77T. This study indicates that majority of the breakthrough COVID-19 clinical cases were infected with the Delta variant, and only 9.8% cases required hospitalization, while fatality was observed in only 0.4% cases. This clearly suggests that the vaccination does provide reduction in hospital admission and mortality.

Spatial Disparities of COVID-19 Cases and Fatalities in United States Counties.

This paper examines the spatial and temporal trends in county-level COVID-19 cases and fatalities in the United States during the first year of the pandemic (January 2020-January 2021). Statistical and geospatial analyses highlight greater impacts in the Great Plains, Southwestern and Southern regions based on cases and fatalities per 100,000 population. Significant case and fatality spatial clusters were most prevalent between November 2020 and January 2021. Distinct urban-rural differences in COVID-19 experiences uncovered higher rural cases and fatalities per 100,000 population and fewer government mitigation actions enacted in rural counties. High levels of social vulnerability and the absence of mitigation policies were significantly associated with higher fatalities, while existing community resilience had more influential spatial explanatory power. Using differences in percentage unemployment changes between 2019 and 2020 as a proxy for pre-emergent recovery revealed urban counties were hit harder in the early months of the pandemic, corresponding with imposed government mitigation policies. This longitudinal, place-based study confirms some early urban-rural patterns initially observed in the pandemic, as well as the disparate COVID-19 experiences among socially vulnerable populations. The results are critical in identifying geographic disparities in COVID-19 exposures and outcomes and providing the evidentiary basis for targeting pandemic recovery.

Locally harvested Covid-19 convalescent plasma could probably help combat the geographically determined SARS-CoV-2 viral variants.

The only effective way to provide individuals with herd immunity against the novel coronavirus [SARS-CoV-2] is to administer an effective vaccine that will help check the current pandemic status. In India, the central drugs standard control organization (CDSCO) has granted the emergency-use authorization [EUA] to three vaccines namely, Covishield (live vaccine, Oxford AstraZeneca, United Kingdom being manufactured by the Serum Institute of India), Covaxin (inactivated vaccine, Bharat Biotech, India) and Sputnik V (live vaccine, Gamaleya, Russia). However, there is a rising need for the efficacy of the vaccines to be proven against the "SARS-CoV-2 viral variants." Also, human plasma is polyclonal in nature with an inherent propensity to identify multiple epitopes of either an antigen or pathogen. With this context in mind, the researchers hypothesize that using COVID-19 convalescent plasma [CCP] harvested from the locally recovered individuals [i.e. potential CCP donors] may be particularly beneficial in combating not only the founder SARS-CoV-2 virus but also the geographically determined SARS-CoV-2 variants among the regionally affected COVID-19 patients.

Factors associated with the spatial heterogeneity of the first wave of COVID-19 in France: a nationwide geo-epidemiological study.

BACKGROUND: The objective of this study was to better understand the factors associated with the heterogeneity of in-hospital COVID-19 morbidity and mortality across France, one of the countries most affected by COVID-19 in the early months of the pandemic. METHODS: This geo-epidemiological analysis was based on data publicly available on government and administration websites for the 96 administrative departments of metropolitan France between March 19 and May 11, 2020, including Public Health France, the Regional Health Agencies, the French national statistics institute, and the Ministry of Health. Using hierarchical ascendant classification on principal component analysis of multidimensional variables, and multivariate analyses with generalised additive models, we assessed the associations between several factors (spatiotemporal spread of the epidemic between Feb 7 and March 17, 2020, the national lockdown, demographic population structure, baseline intensive care capacities, baseline population health and health-care services, new chloroquine and hydroxychloroquine dispensations, economic indicators, degree of urbanisation, and climate profile) and in-hospital COVID-19 incidence, mortality, and case fatality rates. Incidence rate was defined as the cumulative number of in-hospital COVID-19 cases per 100 000 inhabitants, mortality rate as the cumulative number of in-hospital COVID-19 deaths per 100 000, and case fatality rate as the cumulative number of in-hospital COVID-19 deaths per cumulative number of in-hospital COVID-19 cases. FINDINGS: From March 19 to May 11, 2020, hospitals in metropolitan France notified a total of 100 988 COVID-19 cases, including 16 597 people who were admitted to intensive care and 17 062 deaths. There was an overall cumulative in-hospital incidence rate of 155·6 cases per 100 000 inhabitants (range 19·4-489·5), in-hospital mortality rate of 26·3 deaths per 100 000 (1·1-119·2), and in-hospital case fatality rate of 16·9% (4·8-26·2). We found clear spatial heterogeneity of in-hospital COVID-19 incidence and mortality rates, following the spread of the epidemic. After multivariate adjustment, the delay between the first COVID-19-associated death and the onset of the national lockdown was positively associated with in-hospital incidence (adjusted standardised incidence ratio 1·02, 95% CI 1·01-1·04), mortality (adjusted standardised mortality ratio 1·04, 1·02-1·06), and case fatality rates (adjusted standardised fatality ratio 1·01, 1·01-1·02). Mortality and case fatality rates were higher in departments with older populations (adjusted standardised ratio for populations with a high proportion older than aged >85 years 2·17 [95% CI 1·20-3·90] for mortality and 1·43 [1·08-1·88] for case fatality rate). Mortality rate was also associated with incidence rate (1·0004, 1·0002-1·001), but mortality and case fatality rates did not appear to be associated with baseline intensive care capacities. We found no association between climate and in-hospital COVID-19 incidence, or between economic indicators and in-hospital COVID-19 incidence or mortality rates. INTERPRETATION: This ecological study highlights the impact of the epidemic spread, national lockdown, and reactive adaptation of intensive care capacities on the spatial distribution of COVID-19 morbidity and mortality. It provides information for future geo-epidemiological analyses and has implications for preparedness and response policies to current and future epidemic waves in France and elsewhere. FUNDING: None.

Emergency department admissions for myocardial infarction and stroke in France during the first wave of the COVID-19 pandemic: National temporal trends and regional disparities.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic and the national lockdown have led to significant changes in the use of emergency care by the French population. AIMS: To describe the national and regional temporal trends in emergency department (ED) admissions for myocardial infarction (MI) and stroke, before, during and after the first national lockdown. METHODS: The weekly numbers of ED admissions for MI and stroke were collected from the OSCOUR® network, which covers 93.3% of all ED admissions in France. National and regional incidence rate ratios from 02 February until 31 May (2020 versus 2017-2019) were estimated using Poisson regression for MI and stroke, before, during and after lockdown. RESULTS: A decrease in ED admissions was observed for MI (-20% for ST-segment elevation MI and-25% for non-ST-segment elevation MI) and stroke (-18% for ischaemic and-22% for haemorrhagic) during the lockdown. The decrease became significant earlier for stroke than for MI. No compensatory increase in ED admissions was observed at the end of the lockdown for these diseases. Important regional disparities in ED admissions were observed, without correlation with the regional levels of COVID-19 cases. The impact of lockdown on ED admissions was particularly significant in six regions (Ile-de France, Occitanie, Provence-Alpes-Côte d'Azur, Nouvelle Aquitaine, Hauts-de-France and Bretagne). CONCLUSIONS: The decrease in ED admissions for MI and stroke observed during the lockdown was probably caused by fear of COVID-19 and augmented by the lockdown, and was heterogeneous across the French territory. ED admissions were slow to return to the usual levels from previous years, without a compensatory increase. These results underline the need to reinforce messages directed at the population to encourage them to seek care without delay in case of cardiovascular symptoms.

Travel-related control measures to contain the COVID-19 pandemic: an evidence map.

OBJECTIVES: To comprehensively map the existing evidence assessing the impact of travel-related control measures for containment of the SARS-CoV-2/COVID-19 pandemic. DESIGN: Rapid evidence map. DATA SOURCES: MEDLINE, Embase and Web of Science, and COVID-19 specific databases offered by the US Centers for Disease Control and Prevention and the WHO. ELIGIBILITY CRITERIA: We included studies in human populations susceptible to SARS-CoV-2/COVID-19, SARS-CoV-1/severe acute respiratory syndrome, Middle East respiratory syndrome coronavirus/Middle East respiratory syndrome or influenza. Interventions of interest were travel-related control measures affecting travel across national or subnational borders. Outcomes of interest included infectious disease, screening, other health, economic and social outcomes. We considered all empirical studies that quantitatively evaluate impact available in Armenian, English, French, German, Italian and Russian based on the team's language capacities. DATA EXTRACTION AND SYNTHESIS: We extracted data from included studies in a standardised manner and mapped them to a priori and (one) post hoc defined categories. RESULTS: We included 122 studies assessing travel-related control measures. These studies were undertaken across the globe, most in the Western Pacific region (n=71). A large proportion of studies focused on COVID-19 (n=59), but a number of studies also examined SARS, MERS and influenza. We identified studies on border closures (n=3), entry/exit screening (n=31), travel-related quarantine (n=6), travel bans (n=8) and travel restrictions (n=25). Many addressed a bundle of travel-related control measures (n=49). Most studies assessed infectious disease (n=98) and/or screening-related (n=25) outcomes; we found only limited evidence on economic and social outcomes. Studies applied numerous methods, both inferential and descriptive in nature, ranging from simple observational methods to complex modelling techniques. CONCLUSIONS: We identified a heterogeneous and complex evidence base on travel-related control measures. While this map is not sufficient to assess the effectiveness of different measures, it outlines aspects regarding interventions and outcomes, as well as study methodology and reporting that could inform future research and evidence synthesis.

Approaches, Successes, and Challenges in Recruiting Closed Points of Dispensing Sites: A Qualitative Study.

Closed points of dispensing (PODs) are an essential component of local public health preparedness programs because most local public health agencies lack the infrastructure to distribute medical countermeasures to all community members in a short period of time through open PODs alone. However, no study has examined closed POD recruitment strategies or approaches to determine best practices, such as how to select or recruit an agency, group, or business to become a closed POD site once a potential partner has been identified. We conducted qualitative interviews with US disaster planners to identify their approaches and challenges to recruiting closed POD sites. In total, 16 disaster planners participated. Recruitment considerations related to selecting sites, paperwork needed, and challenges faced in recruiting closed POD sites. Important selection criteria for sites included size, agencies or businesses with vulnerable or confined populations who lack access or ability to get to or through open POD sites, and critical infrastructure organizations. Major challenges to recruitment included difficulty convincing sites of closed POD importance, obstacles with recruiting sites that can administer mass vaccination, and fear of legal repercussions related to medical countermeasure dispensing or administration. Closed POD recruitment is a frequently challenging but highly necessary process both before and during the current pandemic. These recommendations can be used by other disaster planners intending to start or expand their closed POD network. Public health agencies should continue working toward improved distribution plans for medical countermeasures, both oral and vaccine, to minimize morbidity and mortality during mass casualty events.

Interstates of Infection: Preliminary Investigations of Human Mobility Patterns in the COVID-19 Pandemic.

PURPOSE: The COVID-19 pandemic has illuminated various heterogeneities between urban and rural environments in public health. The SARS-CoV-2 virus initially spread into the United States from international ports of entry and into urban population centers, like New York City. Over the course of the pandemic, cases emerged in more rural areas, implicating issues of transportation and mobility. Additionally, many rural areas developed into national hotspots of prevalence and transmission. Our aim was to investigate the preliminary impacts of road travel on the spread of COVID-19. This investigation has implications for future public health mitigation efforts and travel restrictions in the United States. METHODS: County-level COVID-19 data were analyzed for spatiotemporal patterns in time-to-event distributions using animated choropleth maps. Data were obtained from The New York Times and the Bureau of the Census. The arrival event was estimated by examining the number of days between the first reported national case (January 21, 2020) and the date that each county attained a given prevalence rate. Of the 3108 coterminous US counties, 2887 were included in the analyses. Data reflect cases accumulated between January 21, 2020, and May 17, 2020. FINDINGS: Animations revealed that COVID-19 was transmitted along the path of interstates. Quantitative results indicated rural-urban differences in the estimated arrival time of COVID-19. Counties that are intersected by interstates had an earlier arrival than non-intersected counties. The arrival time difference was the greatest in the most rural counties and implicates road travel as a factor of transmission into rural communities. CONCLUSION: Human mobility via road travel introduced COVID-19 into more rural communities. Interstate travel restrictions and road travel restrictions would have supported stronger mitigation efforts during the earlier stages of the COVID-19 pandemic and reduced transmission via network contact.

Epidemiology of 631 Cases of COVID-19 Identified in Jiangsu Province Between January 1st and March 20th 2020: Factors Associated with Disease Severity and Analysis of Zero Mortality.

BACKGROUND This retrospective study aimed to investigate the factors associated with disease severity and patient outcomes in 631 patients with COVID-19 who were reported to the Jiangsu Commission of Health between January 1 and March 20, 2020. MATERIAL AND METHODS We conducted an epidemiological investigation enrolling 631 patients with laboratory-confirmed COVID-19 from our clinic from January to March 2020. Patients' information was collected through a standard questionnaire. Then, we described the patients' epidemiological characteristics, analyzed risk factors associated with disease severity, and assessed causes of zero mortality. Additionally, some key technologies for epidemic prevention and control were identified. RESULTS Of the 631 patients, 8.46% (n=53) were severe cases, and no deaths were recorded (n=0). The epidemic of COVID-19 has gone through 4 stages: a sporadic phase, an exponential growth phase, a peak plateau phase, and a declining phase. The proportion of severe cases was significantly different among the 4 stages and 13 municipal prefectures (P<0.001). Factors including age >65 years old, underlying medical conditions, highest fever >39.0°C, dyspnea, and lymphocytopenia (<1.0×109/L) were early warning signs of disease severity (P<0.05). In contrast, earlier clinic visits were associated with better patient outcomes (P=0.029). Further, the viral load was a potentially useful marker associated with COVID-19 infection severity. CONCLUSIONS The study findings from the beginning of the COVID-19 epidemic in Jiangsu Province, China showed that patients who were more than 65 years of age and with comorbidities and presented with a fever of more than 39.0°C developed more severe disease. However, mortality was prevented in this initial patient population by early supportive clinical management.

Countrywide population movement monitoring using mobile devices generated (big) data during the COVID-19 crisis.

Mobile phones have been used to monitor mobility changes during the COVID-19 pandemic but surprisingly few studies addressed in detail the implementation of practical applications involving whole populations. We report a method of generating a "mobility-index" and a "stay-at-home/resting-index" based on aggregated anonymous Call Detail Records of almost all subscribers in Hungary, which tracks all phones, examining their strengths and weaknesses, comparing it with Community Mobility Reports from Google, limited to smartphone data. The impact of policy changes, such as school closures, could be identified with sufficient granularity to capture a rush to shops prior to imposition of restrictions. Anecdotal reports of large scale movement of Hungarians to holiday homes were confirmed. At the national level, our results correlated well with Google mobility data, but there were some differences at weekends and national holidays, which can be explained by methodological differences. Mobile phones offer a means to analyse population movement but there are several technical and privacy issues. Overcoming these, our method is a practical and inexpensive way forward, achieving high levels of accuracy and resolution, especially where uptake of smartphones is modest, although it is not an alternative to smartphone-based solutions used for contact tracing and quarantine monitoring.

Geospatial Analysis of COVID-19: A Scoping Review.

The outbreak of SARS-CoV-2 in Wuhan, China in late December 2019 became the harbinger of the COVID-19 pandemic. During the pandemic, geospatial techniques, such as modeling and mapping, have helped in disease pattern detection. Here we provide a synthesis of the techniques and associated findings in relation to COVID-19 and its geographic, environmental, and socio-demographic characteristics, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology for scoping reviews. We searched PubMed for relevant articles and discussed the results separately for three categories: disease mapping, exposure mapping, and spatial epidemiological modeling. The majority of studies were ecological in nature and primarily carried out in China, Brazil, and the USA. The most common spatial methods used were clustering, hotspot analysis, space-time scan statistic, and regression modeling. Researchers used a wide range of spatial and statistical software to apply spatial analysis for the purpose of disease mapping, exposure mapping, and epidemiological modeling. Factors limiting the use of these spatial techniques were the unavailability and bias of COVID-19 data-along with scarcity of fine-scaled demographic, environmental, and socio-economic data-which restrained most of the researchers from exploring causal relationships of potential influencing factors of COVID-19. Our review identified geospatial analysis in COVID-19 research and highlighted current trends and research gaps. Since most of the studies found centered on Asia and the Americas, there is a need for more comparable spatial studies using geographically fine-scaled data in other areas of the world.

COVID-19 in Ethiopia: a geospatial analysis of vulnerability to infection, case severity and death.

BACKGROUND: COVID-19 has caused a global public health crisis affecting most countries, including Ethiopia, in various ways. This study maps the vulnerability to infection, case severity and likelihood of death from COVID-19 in Ethiopia. METHODS: Thirty-eight potential indicators of vulnerability to COVID-19 infection, case severity and likelihood of death, identified based on a literature review and the availability of nationally representative data at a low geographic scale, were assembled from multiple sources for geospatial analysis. Geospatial analysis techniques were applied to produce maps showing the vulnerability to infection, case severity and likelihood of death in Ethiopia at a spatial resolution of 1 km×1 km. RESULTS: This study showed that vulnerability to COVID-19 infection is likely to be high across most parts of Ethiopia, particularly in the Somali, Afar, Amhara, Oromia and Tigray regions. The number of severe cases of COVID-19 infection requiring hospitalisation and intensive care unit admission is likely to be high across Amhara, most parts of Oromia and some parts of the Southern Nations, Nationalities and Peoples' Region. The risk of COVID-19-related death is high in the country's border regions, where public health preparedness for responding to COVID-19 is limited. CONCLUSION: This study revealed geographical differences in vulnerability to infection, case severity and likelihood of death from COVID-19 in Ethiopia. The study offers maps that can guide the targeted interventions necessary to contain the spread of COVID-19 in Ethiopia.

Geographical Pattern of COVID-19-Related Outcomes over the Pandemic Period in France: A Nationwide Socio-Environmental Study.

BACKGROUND: Several studies have investigated the implication of air pollution and some social determinants on COVID-19-related outcomes, but none of them assessed the implication of spatial repartition of the socio-environmental determinants on geographic variations of COVID-19 related outcomes. Understanding spatial heterogeneity in relation to the socio-environmental determinant and COVID-19-related outcomes is central to target interventions toward a vulnerable population. OBJECTIVES: To determine the spatial variability of COVID-19 related outcomes among the elderly in France at the department level. We also aimed to assess whether a geographic pattern of Covid-19 may be partially explained by spatial distribution of both long-term exposure to air pollution and deprived living conditions. METHODS: This study considered four health events related to COVID-19 infection over the period of 18 March and 02 December 2020: (i) hospitalization, (ii) cases in intensive health care in the hospital, (iii) death in the hospital, and (iv) hospitalized patients recovered and returned back home. We used the percentage of household living in an overcrowding housing to characterize the living conditions and long-term exposure to NO2 to analyse the implication of air pollution. Using a spatial scan statistic approach, a Poisson cluster analysis method based on a likelihood ratio test and Monte Carlo replications was applied to identify high-risk clusters of a COVID-19-related outcome. RESULT: our results revealed that all the outcomes related to COVID-19 infection investigated were not randomly distributed in France with a statistically significant cluster of high risk located in Eastern France of the hospitalization, cases in the intensive health care at the hospital, death in the hospital, and recovered and returned back home compared to the rest of France (relative risk, RR = 1.28, p-value = 0.001, RR = 3.05, p = 0.001, RR = 2.94, p = 0.001, RR = 2.51, p = 0.001, respectively). After adjustments for socio-environmental determinants, the crude cluster shifts according to different scenarios suggested that both the overcrowding housing level and long-term exposure to largely NO2 explain the spatial distribution of COVID-19-related outcomes. CONCLUSIONS: Our findings suggest that the geographic pattern of COVID-19-related outcomes is largely explained by socio-spatial distribution of long-term exposure to NO2. However, to better understand spatial variations of COVID-19-related outcomes, it would be necessary to investigate and adjust it for other determinants. Thus, the current sanitary crisis reminds us of how unequal we all are in facing this disease.

Geographical and Epidemiological Characteristics of 3,487 Confirmed Cases With COVID-19 Among Healthcare Workers in China.

As the first area to report the outbreak, China used to be the front line of the battle against the novel coronavirus SARS-CoV-2. The present descriptive analysis of 3,487 COVID-19-confirmed cases with health workers reported through April 30, 2020 offers important new information to the international community on the epidemic in China. These data showed that Chinese measures including the high-grade protective gear used, mask wearing, and social distancing, are effective in reducing transmission in hospitals.