Significant Spatiotemporal Clusters Research Articles

Spatial and Temporal Analysis of Severe Fever with Thrombocytopenia Syndrome in Anhui Province from 2011 to 2023.

To analyze the spatial autocorrelation and spatiotemporal clustering characteristics of severe fever with thrombocytopenia syndrome(SFTS) in Anhui Province from 2011 to 2023. Data of SFTS in Anhui Province from 2011 to 2023 were collected. Spatial autocorrelation analysis was conducted using GeoDa software, while spatiotemporal scanning was performed using SaTScan 10.0.1 software to identify significant spatiotemporal clusters of SFTS. From 2011 to 2023, 5720 SFTS cases were reported in Anhui Province, with an average annual incidence rate of 0.7131/100,000. The incidence of SFTS in Anhui Province reached its peak mainly from April to May, with a small peak in October. The spatial autocorrelation results showed that from 2011 to 2023, there was a spatial positive correlation(P < 0.05) in the incidence of SFTS in all counties and districts of Anhui Province. Local autocorrelation high-high clustering areas are mainly located in the south of the Huaihe River. The spatiotemporal scanning results show three main clusters of SFTS in recent years: the first cluster located in the lower reaches of the Yangtze River, the eastern region of Anhui Province; the second cluster primarily focused on the region of the Dabie Mountain range, while the third cluster primarily focused on the region of the Huang Mountain range. The incidence of SFTS in Anhui Province in 2011-2023 was spatially clustered.

Spatiotemporal analysis of equine infectious anemia and prediction of risk areas in Europe

Equine Infectious Anemia (EIA) is a vector-borne persistent viral infection in equine animals. The EIA is characterized by recurrent fever, thrombocytopenia, depression, anemia, rapid weight loss, and lower body edema. Control of EIA is achieved through the elimination or isolation of infected animals, resulting in significant economic losses. In recent years, many countries in Europe have experienced outbreaks of EIA, which could potentially develop into a new wave of epidemic and pose a significant threat to the healthy development of the equine industry. This study utilized spatiotemporal analysis techniques and ecological niche modeling to investigate the spatiotemporal distribution characteristics of historical EIA outbreaks and predict risk areas for EIA occurrence in Europe. Spatiotemporal analysis results indicate that from 2005 to 2023, the EIA outbreaks in Europe exhibit five significant spatiotemporal clusters, with hotspots concentrated in southeastern France and northwestern Italy. Ecological niche modeling reveals that western, central, and southern Europe are high-risk areas for EIA outbreaks. Annual mean temperature, annual precipitation, and horse density are important variables that influence the occurrence of EIA. The results of this study can provide decision-makers with valuable insights, helping with EIA monitoring and resource allocation.

Spatiotemporal pattern and suitable areas analysis of equine influenza in global scale (2005-2022).

Equine influenza (EI) is a severe infectious disease that causes huge economic losses to the horse industry. Spatial epidemiology technology can explore the spatiotemporal distribution characteristics and occurrence risks of infectious diseases, it has played an important role in the prevention and control of major infectious diseases in humans and animals. For the first time, this study conducted a systematic analysis of the spatiotemporal distribution of EI using SaTScan software and investigated the important environmental variables and suitable areas for EI occurrence using the Maxent model. A total of 517 occurrences of EI from 2005 to 2022 were evaluated, and 14 significant spatiotemporal clusters were identified. Furthermore, a Maxent model was successfully established with high prediction accuracy (AUC = 0.920 ± 0.008). The results indicated that annual average ultraviolet radiation, horse density, and precipitation of the coldest quarter were the three most important environmental variables affecting EI occurrence. The suitable areas for EI occurrence are widely distributed across all continents, especially in Asia (India, Mongolia, and China) and the Americas (Brazil, Uruguay, USA, and Mexico). In the future, these suitable areas will expand and move eastward. The largest expansion is predicted under SSP126 scenarios, while the opposite trend will be observed under SSP585 scenarios. This study presents the spatial epidemiological characteristics of EI for the first time. The results could provide valuable scientific insights that can effectively inform prevention and control strategies in regions at risk of EI worldwide.

Zoonotic spillover and extreme weather events drive the global outbreaks of airborne viral emerging infectious diseases.

Outbreaks of airborne viral emerging infectious diseases (EIDs) cause an increasing burden on global public health, particularly with a backdrop of intensified climate change. However, infection sources and drivers for outbreaks of airborne viral EIDs remain unknown. Here, we aim to explore the driving mechanisms of outbreaks based on the one health perspective. Outbreak information for 20 types of airborne viral EIDs was collected from the Global Infectious Disease and Epidemiology Network database and a systematic literature review. Four statistically significant and high-risk spatiotemporal clusters for airborne viral EID outbreaks were identified globally using multivariate scan statistic tests. There were 112 outbreaks with clear infection sources, and zoonotic spillover was the most common source (95.54%, 107/112). Since 1970, the majority of outbreaks occurred in healthcare facilities (24.82%), followed by schools (17.93%) and animal-related settings (15.93%). Significant associations were detected between the number of earthquakes, storms, duration of floods, and airborne viral EIDs' outbreaks using a case-crossover study design and multivariable conditional logistic regression. These findings implied that zoonotic spillover and extreme weather events are driving global outbreaks of airborne viral EIDs, and targeted prevention and control measures should be made to reduce the airborne viral EIDs burden.

The Spatiotemporal Impact of Digital Economy on High-Quality Agricultural Development: Evidence from China

China’s high-quality economic development is strongly supported by the high-quality development of agriculture, and the digital economy has emerged as a key driver for promoting shared prosperity and high-quality economic development. Against this backdrop, investigating the connection between high-quality agricultural development and the digital economy holds significant importance. This study utilized the entropy-weighted TOPSIS model to evaluate comprehensive evaluation indicators of the two according to panel data from 30 provinces in China between 2011 and 2021. Subsequently, GIS spatial analysis and exploratory spatial data analysis (ESDA) were employed to investigate the spatiotemporal evolution features and spatial correlations. Finally, the spatiotemporal geographically weighted regression (GTWR) model was constructed to examine the spatiotemporal impact of the digital economy on the advancement of high-quality agricultural growth. The results indicate that: (1) from 2011 to 2021, China’s high-quality agricultural development and digital economy both demonstrated a general increasing trend. In terms of spatial distribution, there were significant spatial variations, with a general trend of “Southeast is higher, whereas the Northwest is lower”. The regions with significant value were primarily clustered in the coastal areas in the east and several provincial capitals. (2) Both of the two exhibited significant global spatial self-correlation, and there were also significant spatiotemporal clustering effects in high-quality agricultural growth, gradually forming a high-value cluster centered around Shanghai and a low-value cluster centered around western provinces. (3) The digital economy positively influences the enhancement of high-quality agricultural development, demonstrating notable spatial and temporal heterogeneity. In contrast to the southeastern areas, the influence is more pronounced in the northern and central-western areas.

Spatiotemporal Patterns and Risk Factors for Newcastle Disease Virus among Chickens in a Tanzania Live Bird Market

Village poultry plays a vital role in providing essential nutrition and income for rural communities in Africa. In this context, poultry are often traded through live bird markets (LBMs), which serve as central trading hubs where producers connect with traders and consumers, facilitating the flow of poultry products along the value chain. While they serve as important trading hubs, these markets create an environment where avian pathogens, like Newcastle disease virus (NDV) and avian influenza virus, can easily emerge and spread. Improving our understanding of the epidemiology of NDV in LBMs is important for assessing disease risks and identifying factors that contribute to its persistence. Local chickens at the Mawenzi LBM in Morogoro municipality were surveyed for NDV presence, its temporal and spatial distribution, and risk factors for NDV infection. Twenty-three percent of 659 local chickens sampled over a 1-year period were positive for NDV based on PCR. Increased odds of NDV infection were identified in chickens that had been in the market for 2 or more days prior to sampling and during the period extending from August through October. Four significant spatiotemporal clusters of NDV-positive chickens encompassing 13 villages were detected between August and October 2020, illustrating geographic hotspots of infection when NDV was most prevalent. Similar to the other LBMs, this market had enclosures with high densities of birds of mixed species, limited biosecurity, and the presence of birds with observable illness. Bird traders who source the chickens from the villages, described long transit times in mixed enclosures with limited sanitation practices and without consideration of sick birds or vaccination status prior to arriving at the LBM. This study highlights the need to invest in improvements to infrastructure and biosecurity for LBMs as well as training opportunities for increasing traders’ knowledge on hygiene and sanitation practices, animal welfare, and poultry biosecurity measures.

Spatiotemporal analysis of fatal earthquakes between 1800 and 2015 at a global scale

Earthquakes are catastrophic natural disasters and along with their aftereffects, they have caused significant fatalities, injuries and economic losses throughout history, and have changed the landscape physically. There is a need to understand the distribution and associated damage patterns of earthquakes to be better prepared and to ensure mitigation of damage in the future. This study analyses the spatial and spatiotemporal trends of earthquake occurrence and associated fatality at a global scale over the 215-year period between 1800 and 2015. Spatial and spatiotemporal analyses revealed that certain countries in Asia including Türkiye, China, India, Pakistan and Indonesia suffered the most both in terms of fatality and earthquake occurrence. There were significant spatiotemporal clusters of earthquake occurrence over this time period on the southern half of Asia, Türkiye and southwest Europe and northern Africa. The findings of the study provide a spatial and spatiotemporal characterization of fatal earthquakes and improve our understanding of these patterns at the global scale. Spatial analyses covering longer time intervals at regional and global scales should be undertaken in future studies to provide a more comprehensive understanding of earthquake occurrence and associated damage patterns

Spatiotemporal distribution characteristics and influencing factors of the rate of cardiovascular hospitalization in Ganzhou city of China.

The objective of this study was to analyze hospitalization rates for cardiovascular diseases (CVD) in Ganzhou City, Jiangxi Province of China from 2015 to 2020 and to uncover the spatiotemporal distribution characteristics and influencing factors, and thus to provide reference for the prevention and control of CVD and public health resources planning. The hospitalization data for CVDs from 2016 to 2020 was obtained from the First Affiliated Hospital of Gannan Medical University, and ArcGIS 10.8, SaTScan 9.5, and Matlab 20.0 were used to analyze the spatial autocorrelation, spatiotemporal scan statistics, and potential affecting factors of the hospitalization rates. The hospitalization rate for CVDs in Ganzhou City showed a slightly increasing trend from 2016 to 2020, with higher rates in winter and summer than that in spring and autumn, and the individuals aged 61 and above constitute a higher proportion compared to other age groups. Additionally, there was a positive correlation between hospitalization rates for CVDs and the counties and districts in Ganzhou City, with high-high aggregation areas mainly distributed in Nankang District, the western urban area of Ganzhou City. The spatial scan analysis identified three different types of significant aggregation areas: high-risk, low-risk, and middle-risk areas. The high-risk area was mainly centered around Zhanggong District or Shangyu County in the central and western regions, with a disease hospitalization rate 2-3 times higher than the rest areas. The study also found that environmental meteorological factors such as the annual average concentration of NO2, O3, average annual temperature, and annual maximum temperature diurnal range had a significant positive effect on hospitalization rates for CVDs in Ganzhou City, with O3 concentration and average annual temperature having significant positive indirect spatial spillover effects. Winter and summer are the seasons with high hospitalization rate of cardiovascular diseases. County residents aged 61 and above are the higher-risk population that needs to pay more attention on for prevention and control of CVD in Ganzhou City, which exhibits significant spatiotemporal clustering. The urban areas of Zhanggong and Nankang in Ganzhou City are the key areas for prevention and control of CVD. The hospitalization rate of CVD in Ganzhou City is influenced by the aforementioned four environmental meteorological factors, with the annual maximum temperature diurnal range showing the most significant positive direct effect.

The COVID-19 pandemic and changes in spatio-temporal patterns of suicide: monthly variations among localities in Argentina

The aim of this study was to assess space-time clustering of suicide in Argentina between 2017 and 2020. A spatio-temporal scan statistic using a space-time permutation model was employed to retrospectively detect clusters of total suicides by sex and by age group. From 2017 to 2020, six statistically significant spatio-temporal clusters were identified in Argentina with either more or less suicides than expected. Two clusters extended from May 2019 to July and September 2020, while the remaining four clusters were detected only during 2020. The start of the COVID-19 pandemic seems to have changed the spatio-temporal patterns of suicide in Argentina. Despite the national decrease in the number of suicides during 2020 compared to previous years, the spatio-temporal analysis revealed both areas with decreases and increases in suicide. This heterogeneous scenario highlights the need for the study of local effects behind the emergence of these spatio-temporal suicide clusters.

Examining spatial variation for immunisation coverage in pregnant women: A nationwide and geospatial retrospective cohort study in Aotearoa New Zealand

BackgroundMaternal influenza and pertussis immunisation is crucial for protecting mothers during pregnancy and their babies in the first weeks of life against severe disease. We examined geospatial variation in maternal immunisation coverage among pregnant women in Aotearoa New Zealand and its health equity implications. MethodWe constructed a retrospective cohort including all pregnant women who delivered between 01 January 2013 and 31 December 2020 using administrative health datasets. Our outcomes were receipt of influenza or pertussis vaccine in any one of three relevant national databases (e.g. National Immunisation Register, Proclaims, or Pharmaceutical collection) during the eligible pregnancy. ResultsData from our retrospective cohort study show significant regional variation in maternal immunisation coverage for both influenza and pertussis from 2013 to 2020. Maximal coverage was around 50% in the best performing regions, which means that half of the women who were pregnant (183,737 women) were not protected. In addition, we found significant spatio-temporal variation and clustering of immunisation coverage. Our findings are interactively available to explore here: https://geohealthlab.shinyapps.io/hapumama/ ConclusionOur study is one of the first to examine spatial variation in maternal vaccination coverage in pregnant women at a national level over space and time. This provides powerful tools to measure the impact of interventions to improve coverage at national and regional levels, with specific reference to inequities between ethnic groups, likely applicable to similar settings internationally.

Examining leopard attacks: spatio-temporal clustering of human injuries and deaths in Western Himalayas, India

Shared spaces in Africa and Asia accommodate both humans and big cats. This engenders rare but distinctive cases of human fatalities by lions, tigers, and leopards. Among big cats, leopards have the widest range and occur even among high densities of humans. This increased potential for encounters with humans results in attacks, exemplified most by India where 50% of the states report human injuries and deaths due to leopards. Himachal Pradesh (HP) state reported 30 lethal and 287 non-lethal leopard attacks on humans per year between 2004 – 2015 (N=317). Identifying patterns in big cat attacks on people facilitates targeted interventions for decreasing such fatalities. This study aims to detect if leopards are cluster-causing agents of human injuries and deaths. We identify the patterns of leopard attacks on humans in Himachal Pradesh by examining the following questions: (a) do leopard-attributed attacks on humans cluster in space and time? and among the leopard-attributed attacks (b) do unprovoked attacks on humans cluster spatio-temporally? and (c) what environmental factors are associated with the clustered leopard attacks on humans? We employed a space-time permutation scan statistic commonly used in epidemiology to test for spatio-temporal clustering of leopard attacks. Attacks were spread across 75% (~42,000 km sq.) of HP in 11 out of 12 districts. We found that 23% of attacks clustered into 12 significant spatio-temporal clusters. Nearly 14% of the leopard-attributed attacks (N=317) were unprovoked and attacks displaying “predatory” signs did not form significant clusters. Binomial regression models were run to test association of eight environmental factors with clustered attacks. We found that leopard-attributed attacks farther away from the protected area boundary and closer to the district boundary had higher probability of clustering. The framework developed in this study to identify the outbreak of unprovoked leopard attacks confirms the absence of dedicated “man-eaters” in the study region. This approach can be applied to adaptively manage human-wildlife conflict and it also demonstrates the utility of scan statistic in ecological research.

Spatio-temporal six-year retrospective study on dermatophytosis in Rio de Janeiro, Southeast Brazil: A tropical tourist locality tale.

Trichophyton, Microsporum, Nannizzia and Epidermophyton genera cause dermatophytosis, the most common and highly contagious infectious skin disease. Rio de Janeiro is one of the most visited cities in the Southern Hemisphere, located in the most visited state of Brazil. This retrospective study investigated epidemiological and laboratorial aspects of dermatophytosis in Rio de Janeiro state, Brazil, by using spatiotemporal analysis. More than half of all individuals were infected by one or more dermatophytes. A variation between 18 and 106 years-old of the studied population was verified, and women more frequently affected. Patients were more frequently infected by Trichophyton spp., most of them T. rubrum, followed by T. mentagrophytes. M. canis and N. gypsea were more frequently isolated in the age group between 40 and 60 years old, while T. rubrum predominates among younger patients. All species presented homogeneous distribution while T. tonsurans appears to be restricted to the Rio de Janeiro capital and E. floccosum to the municipality of Macaé (190 Km apart from RJ). Rio de Janeiro state presented spatial clusters of dermatophytosis with high density in Guanabara Bay (E. floccosum, M. canis, N. gypsea, T. tonsurans) and Niterói (T. rubrum, T. mentagrophytes) but low density in Macaé (E. floccosum). Significant spatiotemporal clusters on dermatophytosis cases were detected in distinct municipalities (p-value ≤ 0.05). The Vulnerability Index (r = 0.293) and Demographic Density (r = 0.652) distributed according to neighborhoods in Niterói were direct related with dermatophytosis cases whereas Income (r = -0.306) was inversely correlated (p-value ≤ 0.05). The dermatophytosis spatiotemporal distinct distribution after two major international events in Rio de Janeiro, Brazil, highlight the pressing need for specific measures of its prevention and controlling. This is particularly relevant in touristic tropical localities which must consider both socio-economical and traveler's medicine variables.

Spatiotemporal monitoring of myxomatosis in European wild rabbit (Oryctolagus cuniculus) in Spanish Mediterranean ecosystems.

A long-term active epidemiological surveillance programme was conducted to determine seroprevalence to myxoma virus (MYXV), infection prevalence and spatiotemporal patterns and factors associated with MYXV circulation in wild rabbits (Oryctolagus cuniculus) in Spanish Mediterranean ecosystems. A total of 2376 animals were sampled over four study periods: 2009-2012 (P1), 2012-2015 (P2), 2015-2018 (P3) and 2018-2021 (P4). Antibodies against MYXV were detected by a commercial indirect ELISA in 59.9% (1424/2376; 95% CI: 58.0-61.9) of wild rabbits. At least one seropositive animal was detected on 131 (96.3%) of 136 game estates sampled. MYXV infection was confirmed by PCR in 94 of 1063 (8.8%; 95% CI: 7.3-10.7) wild rabbits. Circulation of the novel recombinant MYXV (ha-MYXV) was not found in wild rabbits analysed during P4. Five statistically significant spatiotemporal clusters of high MYXV seroprevalence were identified using a Bernoulli model: one in P2 and four in P3. A generalized linear mixed model (GLMM) analysis identified sampling season (autumn), age (adult and juvenile), outbreaks of myxomatosis in the month prior to sampling, mean annual temperature, humidity and seropositivity to rabbit haemorrhagic disease virus as factors potentially linked with MYXV seropositivity. GLMM analysis identified outbreaks of myxomatosis in the month prior to sampling, MYXV seropositivity and presence of lesions compatible with myxomatosis as factors associated with MYXV infection. The results indicate high exposure, widespread but non-homogeneous distribution, and endemic circulation of MYXV in wild rabbit populations in southern Spain during the last decade. Prevalence of antibodies against MYXV showed fluctuations both within the year and over the study periods, revealing variations in the immunity of wild rabbit populations in Mediterranean ecosystems that could increase the risk of MYXV re-emergence in immunologically naïve populations. The present study highlights the importance of long-term surveillance to better understand the epidemiology of MYXV in wild lagomorphs.

Spatiotemporal Clustering of Seismicity in the Kiskatinaw Seismic Monitoring and Mitigation Area

Induced seismicity has become a concern for industry and nearby residents. Fluid-induced earthquakes are a side effect of industrial operations such as hydraulic fracturing, where high-pressure fluids are pumped into the Earth’s crust to increase hydrocarbon flow to a well from petroleum-bearing rock formations. Previous studies on fluid-induced seismicity showed the existence of spatiotemporal clustering and aftershock activity. Here, we study seismic catalogs from the Kiskatinaw Seismic Monitoring and Mitigation Area (KSMMA), British Columbia, Canada to investigate spatiotemporal clustering and determine the aftershock activity due to secondary triggering mechanisms such as static and dynamic stress changes arising from preceding seismic events. Our analysis is based on modern tools of statistical seismology such as the Bi-test and triggering analysis. We find significant aftershock triggering and spatiotemporal clustering across the KSMMA but with large spatial variations, which might be indicative of pre-existing stresses and fault systems. In particular, aftershock triggering is characterized by rapidly decaying spatial distributions of aftershocks beyond the mainshock rupture area and by a strong dominance of small but frequent triggers, consistent with that observed for hydraulic fracturing and saltwater disposal elsewhere.

Spatio-temporal patterns and risk factors of foot-and-mouth disease in Malawi between 1957 and 2019

Foot-and-mouth disease (FMD) is an important livestock disease causing short-term and long-term production losses and hindering local and international trade. To gain access to lucrative foreign markets and also improve local trade, there is a need to employ effective preventive and control strategies. Although FMD has been present in Malawi for over 60 years, little knowledge is available concerning the dynamics and drivers of FMD in the country. A modelling study based on retrospective data was conducted to establish the spatio-temporal distribution and determine the risk factors associated with FMD in Malawi. A retrospective space-time analysis was performed and a matched case-control study was carried out to investigate risk factors. The number of reported FMD outbreaks has descriptively increased after 2000 and the disease has spread to previously unaffected areas. Two significant spatio-temporal clusters of FMD were identified; one in the southern region and the other in the northern region. An analysis of only index cases (first detected locations) also detected two clusters with one in the northern region and the other in the southern region. Higher beef cattle density (p = 0.023), higher pig density (p = 0.043) and increased distance to wildlife protected areas (p = 0.036) were positively associated with the risk of FMD while increased distances to international borders (p = 0.008) and roads (p = 0.034) were associated with reduced risk of FMD. High FMD risk areas were observed in the southern and northern regions but not in the central region during the early years (1957–1981). The more recent increase in FMD risk at the end of the study period (2019) in the central region might be attributed to increases in livestock density in this region. These findings provide insight into the pattern of FMD occurrence that will promote informed decisions for the progressive control of FMD in the region.

Spatiotemporal analysis of reported classical swine fever outbreaks in China (2005-2018) and the influence of weather.

Classical swine fever (CSF) is a viral disease that causes enormous economic losses in the swine industry in endemic countries including China. The aims of the current study were to describe the spatial distribution of annual CSF reports in China from 2005 to 2018, identify spatiotemporal clusters of annual CSF reports during this time period and to investigate the correlations between climate factors (rainfall, wind speed, temperature, vapour pressure and relative humidity) and the occurrence of CSF outbreaks. The strongest (Moran's index>0.19), significant (p<.05) spatial clustering of reported outbreaks was observed during the first 4 years of the study period. This clustering was apparent in the four southern provinces of Guizhou, Guangxi, Guangdong and Yunnan. Five of the six significant (p≤.0001) spatiotemporal clusters occurred during the period 2005-2012. These were widely dispersed, with four clusters persisting for only 1 or 2 years, whereas two clusters (Jiangxi and Yunnan) persisted for 8 and 7 years, respectively. As a result of implementation of a national animal disease control plan and increasing coverage of vaccination, CSF outbreaks in China have generally been controlled and reduced, becoming sporadic in most provinces by 2018. We also confirmed that low relative humidity and high wind speed were significant weather variables associated with the occurrence of CSF. Furthermore, our study has confirmed that CSF is still endemic in some Chinese provinces, and we recommend that the national CSF control protocol be updated and standardized.

Spatiotemporal Pattern of COVID-19-Related Mortality during the First Year of the Pandemic in Brazil: A Population-based Study in a Region of High Social Vulnerability.

ABSTRACT.Currently, the world is facing a severe pandemic caused by the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Although the WHO has recommended preventive measures to limit its spread, Brazil has neglected most of these recommendations, and consequently, our country has the second largest number of deaths from COVID-19 worldwide. In addition, recent studies have shown the relationship between socioeconomic inequalities and the risk of severe COVID-19 infection. Herein, we aimed to assess the spatiotemporal distribution of mortality and lethality rates of COVID-19 in a region of high social vulnerability in Brazil (Northeast region) during the first year of the pandemic. A segmented log-linear regression model was applied to assess temporal trends of mortality and case fatality rate (CFR) and according to the social vulnerability index (SVI). The Local Empirical Bayesian Estimator and Global Moran Index were used for spatial analysis. We conducted a retrospective space–time scan to map clusters at high risk of death from COVID-19. A total of 66,358 COVID-19–related deaths were reported during this period. The mortality rate was 116.2/100,000 inhabitants, and the CFR was 2.3%. Nevertheless, CFR was > 7.5% in 27 municipalities (1.5%). We observed an increasing trend of deaths in this region (AMCP = 18.2; P = 0.001). Also, increasing trends were observed in municipalities with high (N = 859) and very high SVI (N = 587). We identified two significant spatiotemporal clusters of deaths by COVID-19 in this Brazilian region (P = 0.001), and most high-risk municipalities were on the coastal strip of the region. Taken together, our analyses demonstrate that the pandemic has been responsible for several deaths in Northeast Brazil, with clusters at high risk of mortality mainly in municipalities on the coastline and those with high SVI.

Regional distribution of non-human H7N9 avian influenza virus detections in China and construction of a predictive model

IntroductionH7N9 avian influenza has broken out in Chinese poultry 10 times since 2013 and impacted the industry severely. Although the epidemic is currently under control, there is still a latent threat.Material and MethodsEpidemiological surveillance data for non-human H7N9 avian influenza from April 2013 to April 2020 were used to analyse the regional distribution and spatial correlations of positivity rates in different months and years and before and after comprehensive immunisation. In addition, positivity rate monitoring data were disaggregated into a low-frequency and a high-frequency trend sequence by wavelet packet decomposition (WPD). The particle swarm optimisation algorithm was adopted to optimise the least squares support-vector machine (LS-SVM) model parameters to predict the low-frequency trend sequence, and the autoregressive integrated moving average (ARIMA) model was used to predict the high-frequency one. Ultimately, an LS-SVM-ARIMA combined model based on WPD was constructed.ResultsThe virus positivity rate was the highest in late spring and early summer, and overall it fell significantly after comprehensive immunisation. Except for the year 2015 and the single month of December from 2013 to 2020, there was no significant spatiotemporal clustering in cumulative non-human H7N9 avian influenza virus detections. Compared with the ARIMA and LS-SVM models, the LS-SVM-ARIMA combined model based on WPD had the highest prediction accuracy. The mean absolute and root mean square errors were 2.4% and 2.0%, respectively.ConclusionLow error measures prove the validity of this new prediction method and the combined model could be used for inference of future H7N9 avian influenza virus cases. Live poultry markets should be closed in late spring and early summer, and comprehensive H7N9 immunisation continued.

Multifaceted Public Health Response to a COVID-19 Outbreak Among Meat-Processing Workers, Utah, March-June 2020.

To identify potential strategies to mitigate COVID-19 transmission in a Utah meat-processing facility and surrounding community. During March-June 2020, 502 workers at a Utah meat-processing facility (facility A) tested positive for SARS-CoV-2. Using merged data from the state disease surveillance system and facility A, we analyzed the relationship between SARS-CoV-2 positivity and worker demographics, work section, and geospatial data on worker residence. We analyzed worker survey responses to questions regarding COVID-19 knowledge, beliefs, and behaviors at work and home. (1) Facility A workers (n = 1373) with specimen collection dates and SARS-CoV-2 RT-PCR test results; (2) residential addresses of all persons (workers and nonworkers) with a SARS-CoV-2 diagnostic test (n = 1036), living within the 3 counties included in the health department catchment area; and (3) facility A workers (n = 64) who agreed to participate in the knowledge, attitudes, and practices survey. New cases over time, COVID-19 attack rates, worker characteristics by SARS-CoV-2 test results, geospatially clustered cases, space-time proximity of cases among workers and nonworkers; frequency of quantitative responses, crude prevalence ratios, and counts and frequency of coded responses to open-ended questions from the COVID-19 knowledge, attitudes, and practices survey. Statistically significant differences in race (P = .01), linguistic group (P < .001), and work section (P < .001) were found between workers with positive and negative SARS-CoV-2 test results. Geographically, only 6% of cases were within statistically significant spatiotemporal case clusters. Workers reported using handwashing (57%) and social distancing (21%) as mitigation strategies outside work but reported apprehension with taking COVID-19-associated sick leave. Mitigating COVID-19 outbreaks among workers in congregate settings requires a multifaceted public health response that is tailored to the workforce. Tailored, multifaceted mitigation strategies are crucial for reducing COVID-19-associated health disparities among disproportionately affected populations.

Fire risk assessment, spatiotemporal clustering and hotspot analysis in the Luki biosphere reserve region, western DR Congo

This paper analyzes active fires from 2001 to 2019 in and around the Luki Biosphere Reserve, western DR Congo to assess fire risks. In this study, we used descriptive statistics to assess fire events, Getis-Ord G hotspot analysis to define the spatial patterns presented by the fire events. Diagnostics for spatiotemporal clustering of fires location and space-time interaction were assessed the spatiotemporal K function. MODIS data from 2001 to 2019 revealed 4602 fires events and 150,132 ha burned, corresponding to 42.6% of the study area. The results of this study show that the peak of fires was recorded in 2013 and fires are mostly recorded every year during the dry season from June to September. They occurred mostly between Noon and 01:00 PM local time. Fires that occurred in the region had low radiative power with the mean value of 23.5 Mega Watts. The Hotspot region where fires take place is located in the South-Eastern part of the studied area exhibiting a significant spatiotemporal clustering (p value = 0.012). Fires are mainly of 2 origins: annual savannah clearing and agriculture fires. The results of this research will help decision making with proactive preventive measures over time and space.