Suitability of rainwater harvesting in saline and arsenic affected areas of Bangladesh.

A major portion of Bangladesh is currently experiencing a scarcity of safe drinking water because of arsenic contamination, high salinity and human-induced pollution. The objectives of this study were to identify locations with a high scarcity of drinking water and suitability of harvesting rainwater. Kriging interpolation algorithms of Geographical Information System (GIS) was employed to identify the probable water scarce zones as well as suitable zones of harvesting rain water from the available data of secondary sources. Statistical methods were employed to cluster, correlate, and regress variables such as rainfall, salinity, and As. The results showed that groundwater quality in the southwestern parts of Bangladesh is saline with high concentration (>10000 µS/cm). On the other hand, the northeastern and southwestern parts of Bangladesh are also vulnerable to arsenic contamination (60 %-97 % of tubewells), compared to other regions. The rainfall zonation map, covering the years 1951-2022, indicated that the Sylhet division had the highest potential for rainfall (ranging from 2600 to 3900 mm). From this study it was demonstrated that Sylhet, Noakhali, Bhola, Barishall, Patuakhali, Bagerhat, and Khulna were identified as suitable places for sustainable rainwater harvesting (RWH). The findings of this study may play significant role towards achieving sustainable potable water supply in vulnerable zones, if they receive attention from policymakers.

Geospatial determinants and spatio-temporal variation of early initiation of breastfeeding and exclusive breastfeeding in Ethiopia from 2011 to 2019, a multiscale geographically weighted regression analysis.

BACKGROUND: Breastfeeding offers numerous benefits for infants, mothers, and the community, making it the best intervention for reducing infant mortality and morbidity. The World Health Organization (WHO) recommends initiating breastfeeding within one hour after birth and exclusively breastfeeding for the first six months. This study investigated the trend, spatio-temporal variation, and determinants of spatial clustering of early initiation of breastfeeding (EIBF) and exclusive breastfeeding (EBF) in Ethiopia from 2011 to 2019. METHODS: Data from the Ethiopian Demographic and Health Survey (EDHS), which was conducted in 2011, 2016, and 2019, were analyzed utilizing a weighted sample of 10,616 children aged 0-23 years for EIBF and 2,881 children aged 0-5 months for EBF. Spatial autocorrelation analysis was used to measure whether EIBF and EBF were dispersed, clustered, or randomly distributed and Kriging interpolation was employed to predict the outcome variables in the unmeasured areas. Spatial scan statistics were used to identify spatial clusters with a high prevalence of cases. Both global and local regression modeling techniques were employed to examine the spatial relationships between the explanatory variables and the dependent variables. RESULTS: The trend analysis revealed a notable increase in the prevalence of EIBF from 51.8% in 2011 to 71.9% in 2019. Similarly, the prevalence of EBF increased from 52.7% in 2011 to 58.9% in 2019. Spatial analysis demonstrated significant spatial variation in both EIBF and EBF throughout the country. Cold spots or clusters with a low prevalence of EIBF were observed consistently in the Tigray and Amhara regions, and significant cold spot areas of EBF were observed consistently in the Afar and Somali regions. Multiscale geographically weighted regression analysis revealed significant predictors of spatial variations in EIBF, including the religious affiliation of being a follower of the orthodox religion, parity of 1-2, absence of antenatal care visits, and delivery via cesarean section. CONCLUSIONS: Despite the increase in both EIBF and EBF rates over time in Ethiopia, these rates still fall below the national target. To address this issue, the government should prioritize public health programs aimed at improving maternal healthcare service utilization and maternal education. It is essential to integrate facility-level services with community-level services to achieve optimal breastfeeding practices. Specifically, efforts should be made to promote breastfeeding among mothers who have delivered via cesarean section. Additionally, there should be a focus on encouraging antenatal care service utilization and adapting maternal healthcare services to accommodate the mobile lifestyle of pastoralist communities. These steps will contribute to enhancing breastfeeding practices and achieving better outcomes for maternal and child health.

Spatio-temporal clustering of African swine fever virus (Asfarviridae: Asfivirus) circulating in the Kaliningrad region based on three genome markers.

INTRODUCTION: The rapid spread of African swine fever in the Kaliningrad region makes it necessary to use the methods of molecular epidemiology to determine the dynamics and direction of ASF spread in this region of Russia. The aim of the study was to determine single nucleotide polymorphisms within molecular markers K145R, O174L and MGF 505-5R of ASFVs isolated in Kaliningrad region and to study the circulating of the pathogen in European countries by subgenotyping and spatio-temporal clustering analysis. MATERIALS AND METHODS: Blood samples from living domestic pigs and organs from dead domestic pigs and wild boars, collected in the Kaliningrad region between 2017 and 2022 were used. Virus isolation was carried out in porcine bone-marrow primary cell culture. Amplicons of genome markers were amplified by PCR with electrophoretic detection and subsequent extraction of fragments from agarose gel. Sequencing was performed using the Sanger method. RESULTS: The circulation of two genetic clusters of ASFV isolates on the territory of the Kaliningrad has been established: epidemic (K145R-III, MGF 505-5R-II, O174L-I - 94.3% of the studied isolates) and sporadic (K145R-II, MGF 505-5R-II, O174L-I - 5.7%). CONCLUSION: The broaden molecular genetic surveillance of ASFV isolates based on sequencing of genome markers is necessary in the countries of the Eurasian continent to perform a more detailed analysis of ASF spread between countries and within regions.

Large-scale deployment of intelligent transportation to help achieve low-carbon and clean sustainable transportation.

Sustained deep emission reduction in road transportation is encountering bottleneck. The Intelligent Transportation-Speed Guidance System (ITSGS) is anticipated to overcome this challenge and facilitate the achievement of low-carbon and clean transportation. Here, we compiled vehicle emission datasets collected from real-world road experiments and identified the mapping relationships between four pollutants (CO2, CO, NOx, and THC) and their influencing factors through machine learning. We developed random forest models for each pollutant and achieved strong predictive performance, with an R2 exceeding 0.85 on the test dataset for all models. The environmental benefits of ITSGS at the urban scale were quantified by combining emission models with large-scale real trajectory data from Zibo, Shandong Province. Based on temporal and spatial analyses, we found that ITSGS has varying degrees of emission reduction potential during the morning peak, flat peak, and evening peak hours. Values can range from 5.71 %-8.16 % for CO2 emissions, 13.63 %-16.25 % for NOx emissions, 13.69 %-16.45 % for CO emissions, and 4.84-7.07 % for THC emissions, respectively. Additionally, ITSGS can significantly expand the area of low transient emission zones. The best time for achieving maximum environmental benefits from ITSGS is during the workday flat peak. ITSGS limits high-speed and aggressive driving behavior, thereby smoothing the driving trajectory, reducing the frequency of speed switches, and lowering road traffic emissions. The results of the ITSGS environmental benefits evaluation will provide new insights and solutions for sustainable road traffic emission reduction. SYNOPSIS: Large-scale deployment of Intelligent Transportation - Speed Guidance System is a sustainable solution to help achieve low-carbon and clean transportation.

Gonorrhea cluster detection in Manitoba, Canada: Spatial, temporal, and spatio-temporal analysis.

In Canada, Gonorrhea infection ranks as the second most prevalent sexually transmitted infection. In 2018, Manitoba reported an incidence rate three times greater than the national average. This study aims to investigate the spatial, temporal, and spatio-temporal patterns of Gonorrhea infection in Manitoba, using individual-level laboratory-confirmed administrative data provided by Manitoba Health from 2000 to 2016. Age and sex patterns indicate that females are affected by infections at younger ages compared to males. Moreover, there is an increase in repeated infections in 2016, accounting for 16% of the total infections. Spatial analysis at the 96 Manitoba regional health authority districts highlights significant positive spatial autocorrelation, demonstrating a clustered distribution of the infection. Northern districts of Manitoba and central Winnipeg were identified as significant clusters. Temporal analysis shows seasonal patterns, with higher infections in late summer and fall. Additionally, spatio-temporal analysis reveals clusters during high-risk periods, with the most likely cluster in the northern districts of Manitoba from January 2006 to June 2014, and a secondary cluster in central Winnipeg from June 2004 to November 2012. This study identifies that Gonorrhea infection transmission in Manitoba has temporal, spatial, and spatio-temporal variations. The findings provide vital insights for public health and Manitoba Health by revealing high-risk clusters and emphasizing the need for focused and localized prevention, control measures, and resource allocation.

Mortality due to visceral leishmaniasis in Brazil by municipalities, 2001-2018: a spatial-temporal analysis of estimates from the Global Burden of Disease study.

OBJECTIVES: In the American regions, Brazil accounts for 97% of visceral leishmaniasis (VL) cases, with a case fatality rate of approximately 10%. This study aimed to investigate the VL mortality distribution in Brazil and identify high-priority and high-risk areas for intervention strategies. STUDY DESIGN: This was an ecological study that analysed the spatial-temporal patterns of VL mortality in Brazilian municipalities. METHODS: Age-standardised VL mortality rates from the Global Burden of Disease study from 2001 to 2018 were used. The distribution of mortality in the municipalities was assessed, and subsequently the Local Index of Spatial Autocorrelation (LISA) analysis was conducted to identify contiguous areas with high mortality rates. Scan analysis identified clusters of high spatial-temporal risks. RESULTS: The highest mortality rates and clusters were in municipalities located in the Northeast region and in the states of Tocantins and Roraima (North region), Mato Grosso do Sul (Central-West region), and Minas Gerais (Southeast region). According to LISA, there was an increase in the number of municipalities classified as high priority from the first 3-year period (n = 434) to the last 3-year period (n = 644). The spatio-temporal analysis identified 21 high-risk clusters for VL mortality. CONCLUSION: Areas with a high risk of VL mortality should prioritise preventing transmission, invest in early diagnosis and treatment, and promote the training of healthcare professionals.

Evaluating the effects of supervised consumption sites on housing prices in Montreal, Canada using interrupted time series and hedonic price models.

Background: In 2017, three brick and mortar supervised consumption sites (SCS) opened in Montreal, Canada. Opponents argued the sites would attract people who use drugs and reduce local real estate prices. Methods: We used interrupted time series and hedonic price models to evaluate the effects of Montreal's SCS on local real estate prices. We linked the Quebec Professional Association of Real Estate Brokers' housing sales data provided by Centris Inc. with census tract data and gentrification scores. Homes sold within 200 m of the SCS locations between 1 January 2014 and 31 December 2021 were included. We adjusted for internal (e.g., number of bed/bathrooms, unit size) and external attributes (e.g., neighbourhood demographics), and included a spatio-temporal lag to account for correlation between sales. For sensitivity analysis we used site-specific dummy variables to better account for unmeasured neighbourhood differences, and repeated analyses using 500 m and 1000 m radii. Results: We observed a price shock after the opening of the first two SCS in June 2017 (level effect: -10.5%, 95% CI: -19.1%, -1.1%) but prices rose faster month-to-month (trend effect: 1.1%, 95% CI: 0.7%, 1.6%) after implementation. Following the implementation of the third site in November 2017 there was no immediate impact (level effect: 2.4%, 95% CI: -10.4%, 17.0%) but once more prices roses faster (0.9%, 95% CI: 0.4%, 1.5%) thereafter. When we replaced neighbourhood attributes with a site-specific dummy variable, we observed the same pattern. Sales' prices dropped (level effect: -9.6%, 95% CI: -15.0%, -3.8%) but rose faster month-to-month (trend effect: 0.9%, 95% CI: 0.6%, 1.2%) following June 2017's SCS implementations, with no level effect (4.9%, 95% CI: -7.3%, 18.6%) and a positive trend (0.9%, 95% CI: 0.5%, 1.3%) after November 2017's SCS opening. In most 500 m and 1000 m radii models, there were no immediate shocks following SCS opening, however, positive trend effects persisted in all models. Conclusion: Our models suggest homes sold near SCS may experience a price shock immediately post-implementation, with evidence of market recovery in the months that follow.

High-resolution short-term prediction of the COVID-19 epidemic based on spatial-temporal model modified by historical meteorological data.

In the global challenge of Coronavirus disease 2019 (COVID-19) pandemic, accurate prediction of daily new cases is crucial for epidemic prevention and socioeconomic planning. In contrast to traditional local, one-dimensional time-series data-based infection models, the study introduces an innovative approach by formulating the short-term prediction problem of new cases in a region as multidimensional, gridded time series for both input and prediction targets. A spatial-temporal depth prediction model for COVID-19 (ConvLSTM) is presented, and further ConvLSTM by integrating historical meteorological factors (Meteor-ConvLSTM) is refined, considering the influence of meteorological factors on the propagation of COVID-19. The correlation between 10 meteorological factors and the dynamic progression of COVID-19 was evaluated, employing spatial analysis techniques (spatial autocorrelation analysis, trend surface analysis, etc.) to describe the spatial and temporal characteristics of the epidemic. Leveraging the original ConvLSTM, an artificial neural network layer is introduced to learn how meteorological factors impact the infection spread, providing a 5-day forecast at a 0.01° × 0.01° pixel resolution. Simulation results using real dataset from the 3.15 outbreak in Shanghai demonstrate the efficacy of Meteor-ConvLSTM, with reduced RMSE of 0.110 and increased R 2 of 0.125 (original ConvLSTM: RMSE = 0.702, R 2 = 0.567; Meteor-ConvLSTM: RMSE = 0.592, R 2 = 0.692), showcasing its utility for investigating the epidemiological characteristics, transmission dynamics, and epidemic development.

Cetacean Stranding Response Program and Spatial-Temporal Analysis in Taiwan, 1994-2018.

A national cetacean stranding response program in Taiwan has evolved significantly in the past three decades. Initially co-ordinated by National Taiwan University from 1994, the program transitioned to the Taiwan Cetacean Society in 1999, and local governments took on a more prominent role after 2009. A comprehensive stranding database (1994-2018) has been maintained, which documented 1320 stranding events involving 1698 animals from at least 27 species. The most commonly stranded species include finless porpoises, bottlenose dolphins, Kogia spp., and Risso's dolphins. The stranding rates varied annually and seasonally, with increases noted from an average of 16 events per year for the first 3 years to 44-58 events per year between 1997 and 2015, and a sharp rise to over 90 events per year for the period of the last three years. Seasonal variations were also significant, with higher stranding rates during the northeastern monsoon (NEM, October to next April) than that during southwestern monsoon (SWM, May to September). From the aspect of distribution, more frequent and even strandings occurred along the coast of northern Taiwan, while mass strandings were concentrated in the southwestern counties during NEM. Among all strandings, 390 events (29.5%) and 660 animals (38.9%) were live ones. Under great effort in rescuing and rehabilitating 52 cases, 15 cetacean individuals have been released since 2000. Additionally, there have been 56 mass strandings involving at least 11 species since 1994, predominated by pygmy killer whales, particularly during the NEM season along the southwest coast. This study not only contributes to our understanding of the stranding patterns and diversity of the cetaceans in Taiwan, but also provides valuable insights for future conservation strategies on cetaceans in the western Pacific.

A Perspective of the Epidemiology of Rabies in South Africa, 1998-2019.

Despite the implementation of various control strategies aimed at eliminating canine-mediated rabies, the disease is still endemic in up to 150 countries across the world. Rabies remains endemic to South Africa, with various reservoir species (both wildlife species and domestic dogs) capable of maintaining rabies infection, and the epidemiology of the disease is yet to be adequately defined. As such, this study used surveillance data collected between 1998 and 2019 from the two diagnostic laboratories in the country for a statistical space-time analysis to determine regions where significant disease clusters could occur. In addition, the robustness of surveillance activities across the country was evaluated through the mathematical evaluation and visualization of testing rates based on the average number of samples tested per species group. In our study, various significant disease clusters were detected for domestic animals, wildlife and livestock. The significant disease clusters for domestic animals and livestock were primarily restricted to eastern South Africa, while the significant disease clusters in wildlife species were detected across northern and western South Africa. Furthermore, the testing rates identified districts from various provinces where surveillance activities could be considered inadequate, consequently influencing the geographical range of the observed clusters. These results could be used to direct intervention campaigns towards high-risk areas, while also allocating the required resources to improve surveillance in the surrounding areas where surveillance was deemed inadequate.

Geographical coverage of SARS-CoV-2 screening and care centers in Haiti: what do national surveillance data tell us?

BACKGROUND: In Haiti, reported incidence and mortality rates for COVID-19 were lower than expected. We aimed to analyze factors at communal and individual level that might lead to an underestimation of the true burden of the COVID-19 epidemic in Haiti during its first two years. METHODS: We analyzed national COVID-19 surveillance data from March 2020 to December 2021, to describe the epidemic using cluster detection, time series, and cartographic approach. We performed multivariate Quasi-Poisson regression models to determine socioeconomic factors associated with incidence and mortality. We performed a mixed-effect logistic regression model to determine individual factors associated with the infection. RESULTS: Among the 140 communes of Haiti, 57 (40.7%) had a COVID-19 screening center, and the incidence was six times higher in these than in those without. Only 22 (15.7%) communes had a COVID-19 care center, and the mortality was five times higher in these than in those without. All the richest communes had a COVID-19 screening center while only 30.8% of the poorest had one. And 75% of the richest communes had a COVID-19 care center while only 15.4% of the poorest had one. Having more than three healthcare workers per 1000 population in the commune was positively associated with the incidence (SIR: 3.31; IC95%: 2.50, 3.93) and the mortality (SMR: 2.73; IC95%: 2.03, 3.66). At the individual level, male gender (adjusted OR: 1.11; IC95%: 1.01, 1.22), age with a progressive increase of the risk compared to youngers, and having Haitian nationality only (adjusted OR:2.07; IC95%: 1.53, 2.82) were associated with the infection. CONCLUSIONS: This study highlights the weakness of SARS-CoV-2 screening and care system in Haiti, particularly in the poorest communes, suggesting that the number of COVID-19 cases and deaths were probably greatly underestimated.

Growing or declining penalties? A cross-temporal analysis of unemployment scars in the German labor market.

We know that unemployment leaves scars. Unemployment scars are the penalties in terms of employment outcomes that workers experience due to past unemployment. To date we lack a long-term longitudinal account which examines how unemployment scarring has developed over time. The aim of this article is to fill this gap. We draw on longitudinal data from the German Socio-Economic Panel spanning a period of more than 30 years, from 1985 to 2020, and investigate long-term time trends of unemployment scarring. The German labor market has experienced profound structural and institutional change over the past decades. These changes have been associated with increased inequalities in the labor market. We examine whether the substantial transformation of the German labor market also had repercussions for the extent of post-unemployment penalties. We focus on employment probabilities and wages, and consider both short-term (two years after the unemployment incidence) and mid-term outcomes (four years after the unemployment incidence). Changes in the amount of unemployment scarring over time can also occur due to changes in the composition of the unemployed. Our analyses therefore do not only investigate how macro-economic and institutional change are associated with varying amounts of unemployment scarring, but also control for and examine the role of compositional change.

Geospatial patterns of excess mortality in Belgium: Insights from the first year of the COVID-19 pandemic.

OBJECTIVES: Belgium experienced multiple COVID-19 waves that hit various groups in the population, which changed the mortality pattern compared to periods before the pandemic. In this study, we investigated the geographical excess mortality trend in Belgium during the first year of the COVID-19 pandemic. METHODS: We retrieved the number of deaths and population data in 2020 based on gender, age, and municipality of residence, and we made a comparison with the mortality data in 2017-2019 using a spatially discrete model. RESULTS: Excess mortality was significantly associated with age, gender, and COVID-19 incidence, with larger effects in the second half of 2020. Most municipalities had higher risks of mortality with a number of exceptions in the northeastern part of Belgium. Some discrepancies in excess mortality were observed between the north and south regions. CONCLUSIONS: This study offers useful insight into excess mortality and will aid local and regional authorities in monitoring mortality trends.

Comparative analysis of machine learning methods for prediction of chlorophyll-a in a river with different hydrology characteristics: A case study in Fuchun River, China.

Eutrophication is a serious threat to water quality and human health, and chlorophyll-a (Chla) is a key indicator to represent eutrophication in rivers or lakes. Understanding the spatial-temporal distribution of Chla and its accurate prediction are significant for water system management. In this study, spatial-temporal analysis and correlation analysis were applied to reveal Chla concentration pattern in the Fuchun River, China. Then four exogenous variables (wind speed, water temperature, dissolved oxygen and turbidity) were used for predicting Chla concentrations by six models (3 traditional machine learning models and 3 deep learning models) and compare the performance in a river with different hydrology characteristics. Statistical analysis shown that the Chla concentration in the reservoir river segment was higher than in the natural river segment during August and September, while the dominant algae gradually changed from Cyanophyta to Cryptophyta. Moreover, air temperature, water temperature and dissolved oxygen had high correlations with Chla concentrations among environment factors. The results of the prediction models demonstrate that extreme gradient boosting (XGBoost) and long short-term memory neural network (LSTM) were the best performance model in the reservoir river segment (NSE = 0.93; RMSE = 4.67) and natural river segment (NSE = 0.94; RMSE = 1.84), respectively. This study provides a reference for further understanding eutrophication and early warning of algal blooms in different type of rivers.

Secular trends and sociodemographic, biological, and behavioural factors associated with handgrip strength in adolescents in southern Brazil: An allometric approach.

The purpose of this study was to analyse secular trend in handgrip strength (HGS) in adolescents using an allometric approach and identify the factors associated. The sample comprised 657 and 1004 adolescents (14 to 19 years) in 2007 and 2017/2018, respectively, of public schools in Florianópolis, Brazil. The dependent variable was HGS normalised to body mass and height. Covariance analysis was used to examine secular trends in HGS, and multiple linear regression was used to identify associated factors. The independent variables were sociodemographic, biological, and behavioural factors. Comparison of HGS between surveys indicated a negative secular trend in both sexes (p < 0.001). In boys, there was a positive association of HGS with age and FFM in both surveys. In 2017/18, there was a positive association with sexual maturation and a negative association with sitting time and fat percentage. In girls, FFM was positively associated with HGS in both surveys. In 2007, there were positive associations of HGS with age and vigorous physical activity, whereas, in 2017/18, negative associations were observed with economic level and sitting time. The findings of the present study show a decline in adolescent HGS. And behavioural changes appear to be contributing to declines in HGS.

Dynamic evolution and trend prediction in coupling coordination between urban and rural space utilization efficiency based local and tele-coupling model.

Optimizing the pattern of territorial space utilization is one of the key tasks to achieve the sustainable development goals. With the accelerating rate of global urbanization, the understanding of territorial space utilization efficiency, role and potential is a prerequisite for alleviating contradictions in urban and rural space distribution. The city cluster is the main form of organization for urban development in future, so the study attempted to explore the urban and rural space utilization efficiency (URSUE) in Chengdu-Chongqing urban agglomeration (CCEC) from coupling coordination degree (CCD) perspective. Considering the gradual increase in the trend of remote interactions between URSUE, we further introduced the Local and Tele-coupling coordination (LTCCD) model that takes into account interactive development relationship between different systems. The results of the study show that: In CCEC, the more economically developed cities indicated that urban spatial utilization efficiency lags behind rural spatial utilization efficiency; The LTCCD in the geographic center region will indicate a higher level but the LTCCD in the economic core cities is higher compared with their CCD level, especially in Chengdu City. This suggests that the LTCCD model is better able to take into account regional development correlations and spatial spillovers effect. This study attempts to explore several key issues of urban-rural spatial allocation in the process of urbanization development and to provide guidance for the territorial space utilization planning in urban agglomerations.

Exploring Dengue Dynamics: A Multi-Scale Analysis of Spatio-Temporal Trends in Ibagué, Colombia.

Our study examines how dengue fever incidence is associated with spatial (demographic and socioeconomic) alongside temporal (environmental) factors at multiple scales in the city of Ibagué, located in the Andean region of Colombia. We used the dengue incidence in Ibagué from 2013 to 2018 to examine the associations with climate, socioeconomic, and demographic factors from the national census and satellite imagery at four levels of local spatial aggregation. We used geographically weighted regression (GWR) to identify the relevant socioeconomic and demographic predictors, and we then integrated them with environmental variables into hierarchical models using integrated nested Laplace approximation (INLA) to analyze the spatio-temporal interactions. Our findings show a significant effect of spatial variables across the different levels of aggregation, including human population density, gas and sewage connection, percentage of woman and children, and percentage of population with a higher education degree. Lagged temporal variables displayed consistent patterns across all levels of spatial aggregation, with higher temperatures and lower precipitation at short lags showing an increase in the relative risk (RR). A comparative evaluation of the models at different levels of aggregation revealed that, while higher aggregation levels often yield a better overall model fit, finer levels offer more detailed insights into the localized impacts of socioeconomic and demographic variables on dengue incidence. Our results underscore the importance of considering macro and micro-level factors in epidemiological modeling, and they highlight the potential for targeted public health interventions based on localized risk factor analyses. Notably, the intermediate levels emerged as the most informative, thereby balancing spatial heterogeneity and case distribution density, as well as providing a robust framework for understanding the spatial determinants of dengue.

Genetic variability of histidine-rich protein 2 repeat sequences: Misleading factor in true determination of Plasmodium falciparum in different population.

PURPOSE: Genetically diverse parasites enhances resistance against antimalarials, vaccines and host immune responses. The present study was designed to evaluate the role played by Plasmodium falciparum genetic diversity in predicting the real world malarial population. METHODS: Initially, the incidence pattern of all four northern Indian malarial species was examined using 18S rRNA gene and performed principal component analysis (PCA) based on frequencies of Plasmodium species. Consequently, genetic variance of Plasmodium falciparum histidine-rich protein-2 (Pfhrp2) gene among different malarial populations were compared using phylogenetic analysis. Multi-dimensional scaling was performed to assess genetic similarities and distances among studied populations. RESULTS: Of total 2168 patients screened, 561 patients with fever of unknown origin were included. 18S rRNA and Pfhrp2 genes were amplified in 78 and 45 samples, respectively. Among them 13.9%(78/561) patients had Plasmodium infection. Infections by P. falciparum, P. vivax and mixed infections were diagnosed among 47(60.2%) and 28(35.9%) and 3(3.8%) patients, respectively. We found eight types of Pfhrp2 amino acid sequence repeats among northern Indian population. The PCA findings were in line with genetic diversity and phylogenetic data. Temporal analysis showed the proportion of total diversity present in total subpopulation (ΔS/ΔT) was maximum for P. falciparum. CONCLUSIONS: Higher incidence of Pfhrp2 sequence variation through genetic recombination among multiple strains during sexual reproduction is potentially correlated with high transmission activity. This sequence variation might alter RDT detection sensitivities for different parasites by modulating the structure and frequency of antigenic epitopes.

SARS-CoV-2 Pandemic in a Small-Sized Municipality in Ceará State, Brazil: Temporal and Spatial Evolution.

Data on the temporal and spatial evolution of SARS-CoV-2 and local control measures and their effects on morbidity and mortality patterns in rural Brazil are scarce. We analyzed the data from case notification systems, epidemiological investigation reports, and municipal decrees in Itapajé, a small municipality in Ceará State in northeast Brazil. For spatial and spatio-temporal analyses, cases and deaths were mapped. There were a total of 3020 cases of COVID-19, recorded between April 2020 and December 2021; 135 (4.5%) died. The cumulative incidence and mortality rates were 5650.3 cases and 252.6 deaths per 100,000 people, respectively. The index case of SARS-CoV-2 in Itapajé was diagnosed in March 2020. The first peak of cases and deaths occurred in May 2020. The second wave peaked in May 2021, with the highest number of deaths in March 2021. According to the spatial analysis, the highest density of cases and deaths occurred in the central urban areas. In these areas, there were also the clusters of highest risk according to the spatio-temporal analyses. The municipal government issued 69 decrees on restriction measures, surveillance, and the maintenance of social isolation as a response to the pandemic. The spread of the SARS-CoV-2 pandemic in Itapajé mirrored the dynamics in large metropolitan regions, going from central neighborhoods of low socio-economic status to the wealthier peripheries.

Assessing the Progress toward a Water-Efficient Economy in the United States from 1985 to 2015.

United Nations Sustainable Development Goal 6 tackles the long-neglected economic dimension of water utilization by monitoring nations' water use efficiency (WUE). However, it is imperative to emphasize the need for consistent spatial-temporal subnational WUE estimates, rather than relying solely on recent national trends, which can obscure crucial water use concerns and improvement opportunities. Here, a time series analysis of national, state, and sectoral (e.g., industrial, service, and agriculture) WUE from 1980 to 2015 was developed by compiling the most comprehensive and disaggregated water and economic data from 3243 US counties and 50 US states. The US total WUE increased by 181% from 16.2 (1985) to 45.6 USD/m3 (2015), driven by service sector WUE enhancements. The increased industry and service WUEs in most states were more strongly correlated with decreased per capita water withdrawal than with economic growth. Simultaneously, reductions in agriculture WUE were observed in 18 states potentially because of the complicated interaction of diverse factors specific to local communities. Expanding WUE gaps between affluent and less affluent states, and persisting WUE gaps between water-abundant andwater-scarce states highlight the need to advance policies to support under-resourced communities in effective water planning and water pricing for advancing equitable development.