Mapping malaria transmission foci in Northeast Thailand from 2011 to 2021: approaching elimination in a hypoendemic area.

BACKGROUND: Thailand is approaching local elimination of malaria in the eastern provinces. It has successfully reduced the number of cases over the past decade, but there are persistent transmission hot spots in and around forests. This study aimed to use data from the malaria surveillance system to describe the spatiotemporal trends of malaria in Northeast Thailand and fine-scale patterns in locally transmitted cases between 2011 and 2021. METHODS: Case data was stratified based on likely location of infection and parasite species. Annual Parasite Index per 1000 population (API) was calculated for different categories. Time series decomposition was performed to identify trends and seasonal patterns. Statistically significant clusters of high (hot spots) and low (cold spots) API were identified using the Getis-Ord Gi* statistic. The stability of those hot spots and the absolute change in the proportion of API density from baseline were compared by case type. RESULTS: The total number of confirmed cases experienced a non-linear decline by 96.6%, from 1061 in 2011 to 36 in 2021. There has been a decline in both Plasmodium vivax and Plasmodium falciparum case numbers, with only four confirmed P. falciparum cases over the last two years-a 98.89% drop from 180 in 2011. API was generally higher in Si Sa Ket province, which had peaks every 2-3 years. There was a large outbreak in Ubon Ratchathani in 2014-2016 which had a high proportion of P. falciparum reported. The proportion of cases classified increased over the study period, and the proportion of cases classed as indigenous to the village of residence increased from 0.2% to 33.3%. There were stable hot spots of indigenous and imported cases in the south of Si Sa Ket and southeast of Ubon Ratchathani. Plasmodium vivax hot spots were observed into recent years, while those of P. falciparum decreased to zero in Ubon in 2020 and emerged in the eastern part in 2021, the same year that P. falciparum hot spots in Si Sa Ket reached zero. CONCLUSIONS: There has been a large, non-linear decline in the number of malaria cases reported and an increasing proportion of cases are classed as indigenous to the patient's village of residence. Stable hot spots of ongoing transmission in the forested border areas were identified, with transmission likely persisting because of remote location and high-risk forest-going behaviours. Future efforts should include cross-border collaboration and continued targeting of high-risk behaviours to reduce the risk of imported cases seeding local transmission.

Moving beyond hotspots of HIV prevalence to geospatial hotspots of UNAIDS 95-95-95 targets in sub-Saharan Africa.

The HIV epidemic in sub-Saharan Africa displays a varied geographical distribution, with particular regions termed as HIV hotspots due to a higher prevalence of infection. Addressing these hotspots is essential for controlling the epidemic. However, these regions, influenced by historical factors, challenge standard interventions. Legacy effects-the lasting impact of past events-play a substantial role in the persistence of these hotspots. To address this challenge of the standard interventions, we propose a shift towards the UNAIDS 95-95-95 targets. Spatial analysis of HIV viral load and antiretroviral therapy coverage can provide a more comprehensive perspective on the epidemic's dynamics. Studies in Zambia and Zimbabwe, using this approach, have revealed disparities in HIV care metrics across regions. By focusing on the UNAIDS 95-95-95 targets, more effective control strategies can be designed, with consideration of both historical and current factors. This approach would offer a solution-oriented strategy, emphasising tailored interventions based on specific regional needs.

Contribution of host species and pathogen clade to snake fungal disease hotspots in Europe.

Infectious diseases are influenced by interactions between host and pathogen, and the number of infected hosts is rarely homogenous across the landscape. Areas with elevated pathogen prevalence can maintain a high force of infection and may indicate areas with disease impacts on host populations. However, isolating the ecological processes that result in increases in infection prevalence and intensity remains a challenge. Here we elucidate the contribution of pathogen clade and host species in disease hotspots caused by Ophidiomyces ophidiicola, the pathogen responsible for snake fungal disease, in 21 species of snakes infected with multiple pathogen strains across 10 countries in Europe. We found isolated areas of disease hotspots in a landscape where infections were otherwise low. O. ophidiicola clade had important effects on transmission, and areas with multiple pathogen clades had higher host infection prevalence. Snake species further influenced infection, with most positive detections coming from species within the Natrix genus. Our results suggest that both host and pathogen identity are essential components contributing to increased pathogen prevalence.

Disease clusters subsequent to anxiety and stress-related disorders and their genetic determinants.

Anxiety/stress-related disorders have been associated with multiple diseases, whereas a comprehensive assessment of the structure and interplay of subsequent associated diseases and their genetic underpinnings is lacking. Here, we first identify 136, out of 454 tested, medical conditions associated with incident anxiety/stress-related disorders attended in specialized care using a population-based cohort from the nationwide Swedish Patient Register, comprising 70,026 patients with anxiety/stress-related disorders and 1:10 birth year- and sex-matched unaffected individuals. By combining findings from the comorbidity network and disease trajectory analyses, we identify five robust disease clusters to be associated with a prior diagnosis of anxiety/stress-related disorders, featured by predominance of psychiatric disorders, eye diseases, ear diseases, cardiovascular diseases, and skin and genitourinary diseases. These five clusters and their featured diseases are largely validated in the UK Biobank. GWAS analyses based on the UK Biobank identify 3, 33, 40, 4, and 16 significantly independent single nucleotide polymorphisms for the link to the five disease clusters, respectively, which are mapped to several distinct risk genes and biological pathways. These findings motivate further mechanistic explorations and aid early risk assessment for cluster-based disease prevention among patients with newly diagnosed anxiety/stress-related disorders in specialized care.

Kidney disease hotspots and water balance in a warming world.

PURPOSE OF REVIEW: Geographically localized areas with a high prevalence of kidney disease exist currently in several regions of the world. Although the exact cause is unclear, environmental exposures accelerated by climate change, particularly heat exposure and ground water contamination, are hypothesized as putative risk factors. Aiming to inform investigations of water-related exposures as risk factors for kidney disease, we excavate the history of major water sources in three regions that are described as hotspots of kidney disease: the low-lying coastal regions in El Salvador and Nicaragua, the dry central region in Sri Lanka, and the Central Valley of California. RECENT FINDINGS: Historic data indicate that these regions have experienced water scarcity to which several human-engineered solutions were applied; these solutions could be hypothesized to increase residents' exposure to putative kidney toxins including arsenic, fluoride, pesticides, and cyanobacteria. Combined with heat stress experienced in context of climate change, there is potential for multistressor effects on kidney function. Climate change will also amplify water scarcity, and even if regional water sources are not a direct risk factor for development of kidney disease, their scarcity will complicate the treatment of the relatively larger numbers of persons with kidney disease living in these hotspots. SUMMARY: Nephrologists and kidney disease researchers need to engage in systematic considerations of environmental exposures as potential risk factors for kidney disease, including water sources, their increasing scarcity, and threats to their quality due to changing climate.

Well-Being of Children and Families in COVID-19 Hotspots in Chicago.

BACKGROUND: Families in high-risk communities for COVID-19 transmission experienced a disproportionate burden during the pandemic. This study assessed these families' needs, changes in children's well-being, and perceptions related to the pandemic. METHODS: Four online surveys were administered January 2021 to September 2021 to parents of students, enrolled in parochial, kindergarten-eighth grade schools in Chicago neighborhoods with higher COVID-19 incidence rates by ZIP code, compared to the city average, and higher resource need. RESULTS: The response rate was 69.1% (n = 186 of 269) in the baseline survey; and other surveys were at 1 (n = 151), 3 (n = 145), and 5 months (n = 154). Of the sample, 83% of parents identified as Hispanic/Latinx with a mean age of 38.3 years (SD: 8.5). Approximately a quarter of parents reported difficulty paying cable and internet bills (26%) and paying utilities (25%). Parents reported children as happy (94% and 95%, p = .59) and hopeful (96% and 95%, p = .74) at 1-month (February to May 2021) and 5-month surveys (June to September 2021). Parents also reported fewer children were irritable (29% vs 19%, p = .03), felt lonely (17% vs 10%, p = .03), and felt isolated (28% vs 9%, p < .001) between those survey waves. The majority (67%) of parents felt that their child had no difficulty wearing a mask in public. CONCLUSIONS: In this longitudinal study, Chicago parents rated children's well-being highly and reported a decrease in negative emotions over time. The areas of need identified may be particularly relevant for outreach and providing resources to Hispanic/Latino families in future emergencies or global health threats.

Hantavirus Disease Cluster Caused by Seoul Virus, Germany.

A cluster of 3 persons in Germany experienced hantavirus disease with renal insufficiency. Reverse transcription PCR-based genotyping revealed infection by Seoul hantavirus transmitted from pet rats. Seoul virus could be responsible for disease clusters in Europe, and infected pet rats should be considered a health threat.

Evolution of spatial disease clusters via a Bayesian space-time variability modelling.

This study proposes to use exceedance posterior probabilities of a space-time random-effects model to study the temporal dynamics of clusters. The local time trends specified for each area is further smoothed over space. We modelled the common spatial and the space-varying temporal trend using a multivariate Markov Random field to incorporate within-area correlations. We estimate the model parameters within a fully Bayesian framework. The exceedance posterior probabilities are further used to classify the common spatial trend into hot-spots, cold-spots, and neutral-spots. The local time trends are classified into increasing, decreasing, and stable trends. The results is a 3×3 table depicting the time trends within clusters. As a demonstration, we apply the proposed methodology to study the evolution of spatial clustering of intestinal parasite infections in Ghana. We find the methodology presented in this paper applicable and extendable to other or multiple tropical diseases which may have different space-time conceptualizations.

Systematic review and meta-analysis of disease clustering in multimorbidity: a study protocol.

INTRODUCTION: Multimorbidity is defined as the presence of two or more chronic diseases. Co-occurring diseases can have synergistic negative effects, and are associated with significant impacts on individual health outcomes and healthcare systems. However, the specific effects of diseases in combination will vary between different diseases. Identifying which diseases are most likely to co-occur in multimorbidity is an important step towards population health assessment and development of policies to prevent and manage multimorbidity more effectively and efficiently. The goal of this project is to conduct a systematic review and meta-analysis of studies of disease clustering in multimorbidity, in order to identify multimorbid disease clusters and test their stability. METHODS AND ANALYSIS: We will review data from studies of multimorbidity that have used data clustering methodologies to reveal patterns of disease co-occurrence. We propose a network-based meta-analytic approach to perform meta-clustering on a select list of chronic diseases that are identified as priorities for multimorbidity research. We will assess the stability of obtained disease clusters across the research literature to date, in order to evaluate the strength of evidence for specific disease patterns in multimorbidity. ETHICS AND DISSEMINATION: This study does not require ethics approval as the work is based on published research studies. The study findings will be published in a peer-reviewed journal and disseminated through conference presentations and meetings with knowledge users in health systems and public health spheres. PROSPERO REGISTRATION NUMBER: CRD42023411249.

Autoantibody status according to multiparametric assay accurately estimates connective tissue disease classification and identifies clinically relevant disease clusters.

OBJECTIVE: Assessment of circulating autoantibodies represents one of the earliest diagnostic procedures in patients with suspected connective tissue disease (CTD), providing important information for disease diagnosis, identification and prediction of potential clinical manifestations. The purpose of this study was to evaluate the ability of multiparametric assay to correctly classify patients with multiple CTDs and healthy controls (HC), independent of clinical features, and to evaluate whether serological status could identify clusters of patients with similar clinical features. METHODS: Patients with systemic lupus erythematosus (SLE), systemic sclerosis (SSc), Sjogren's syndrome (SjS), undifferentiated connective tissue disease (UCTD), idiopathic inflammatory myopathies (IIM) and HC were enrolled. Serum was tested for 29 autoantibodies. An XGBoost model, exclusively based on autoantibody titres was built and classification accuracy was evaluated. A hierarchical clustering model was subsequently developed and clinical/laboratory features compared among clusters. RESULTS: 908 subjects were enrolled. The classification model showed a mean accuracy of 60.84±4.05% and a mean area under the receiver operator characteristic curve of 88.99±2.50%, with significant discrepancies among groups. Cluster analysis identified four clusters (CL). CL1 included patients with typical features of SLE. CL2 included most patients with SjS, along with some SLE and UCTD patients with SjS-like features. CL4 included anti-Jo1 patients only. CL3 was the largest and most heterogeneous, including all the remaining subjects, overall characterised by low titre or lower-prevalence autoantibodies. CONCLUSION: Extended multiparametric autoantibody assay allowed an accurate classification of CTD patients, independently of clinical features. Clustering according to autoantibody titres is able to identify clusters of CTD subjects with similar clinical features, independently of their final diagnosis.

Climate determines transmission hotspots of Polycystic Echinococcosis, a life-threatening zoonotic disease, across Pan-Amazonia.

Polycystic Echinococcosis (PE), a neglected life-threatening zoonotic disease caused by the cestode Echinococcus vogeli, is endemic in the Amazon. Despite being treatable, PE reaches a case fatality rate of around 29% due to late or missed diagnosis. PE is sustained in Pan-Amazonia by a complex sylvatic cycle. The hunting of its infected intermediate hosts (especially the lowland paca Cuniculus paca) enables the disease to further transmit to humans, when their viscera are improperly handled. In this study, we compiled a unique dataset of host occurrences (~86000 records) and disease infections (~400 cases) covering the entire Pan-Amazonia and employed different modeling and statistical tools to unveil the spatial distribution of PE's key animal hosts. Subsequently, we derived a set of ecological, environmental, climatic, and hunting covariates that potentially act as transmission risk factors and used them as predictors of two independent Maximum Entropy models, one for animal infections and one for human infections. Our findings indicate that temperature stability promotes the sylvatic circulation of the disease. Additionally, we show how El Niño-Southern Oscillation (ENSO) extreme events disrupt hunting patterns throughout Pan-Amazonia, ultimately affecting the probability of spillover. In a scenario where climate extremes are projected to intensify, climate change at regional level appears to be indirectly driving the spillover of E. vogeli. These results hold substantial implications for a wide range of zoonoses acquired at the wildlife-human interface for which transmission is related to the manipulation and consumption of wild meat, underscoring the pressing need for enhanced awareness and intervention strategies.

Genetic profiles of Schistosoma haematobium parasites from Malian transmission hotspot areas.

BACKGROUND: Although schistosomiasis is a public health issue in Mali, little is known about the parasite genetic profile. The purpose of this study was to analyze the genetic profile of the schistosomes of Schistosoma haematobium group in school-aged children in various sites in Mali. METHODS: Urine samples were collected from 7 to 21 November 2021 and subjected to a filtration method for the presence S. haematobium eggs. The study took place in two schistosomiasis endemic villages (Fangouné Bamanan and Diakalèl), qualified as hotspots according to the World Health Organization (WHO) definition. Molecular genotyping on both Cox1 and ITS2/18S was used for eggs' taxonomic assignation. RESULTS: A total of 970 miracidia were individually collected from 63 school-aged children and stored on Whatman FTA cards for molecular analysis. After genotyping 42.0% (353/840) and 58.0% (487/840) of miracidia revealed Schistosoma bovis and S. haematobium Cox1 profiles, respectively; 95.7 (885/925) and 4.3% (40/925) revealed S. haematobium and S. haematobium/S. curassoni profiles for ITS/18S genes, respectively. There was a significant difference in the Cox1 and ITS2/18S profile distribution according to the village (P < 0.0001). Overall, 45.6% (360/789) were hybrids, of which 72.0% (322/447) were from Diakalèl. Three hybrids' profiles (Sb/Sc_ShxSc with 2.3%; Sb/Sc_ShxSh with 40.5%; Sh_ShxSc with 2.8%) and one pure profile (Sh_ShxSh with 54.4%) were identified. CONCLUSION: Our findings show, for the first time to our knowledge, high prevalence of hybrid schistosomes in Mali. More studies are needed on population genetics of schistosomes at the human and animal interface to evaluate the parasite's gene flow and its consequences on epidemiology of the disease as well as the transmission to humans.

NAIR: Network Analysis of Immune Repertoire.

T cells represent a crucial component of the adaptive immune system and mediate anti-tumoral immunity as well as protection against infections, including respiratory viruses such as SARS-CoV-2. Next-generation sequencing of the T-cell receptors (TCRs) can be used to profile the T-cell repertoire. We developed a customized pipeline for Network Analysis of Immune Repertoire (NAIR) with advanced statistical methods to characterize and investigate changes in the landscape of TCR sequences. We first performed network analysis on the TCR sequence data based on sequence similarity. We then quantified the repertoire network by network properties and correlated it with clinical outcomes of interest. In addition, we identified (1) disease-specific/associated clusters and (2) shared clusters across samples based on our customized search algorithms and assessed their relationship with clinical outcomes such as recovery from COVID-19 infection. Furthermore, to identify disease-specific TCRs, we introduced a new metric that incorporates the clonal generation probability and the clonal abundance by using the Bayes factor to filter out the false positives. TCR-seq data from COVID-19 subjects and healthy donors were used to illustrate that the proposed approach to analyzing the network architecture of the immune repertoire can reveal potential disease-specific TCRs responsible for the immune response to infection.

Spatial Epidemiologic Analysis and Risk Factors for Nontuberculous Mycobacteria Infections, Missouri, USA, 2008-2019.

Nontuberculous mycobacteria (NTM) infections are caused by environmental exposure. We describe spatial distribution of NTM infections and associations with sociodemographic factors and flooding in Missouri, USA. Our retrospective analysis of mycobacterial cultures reported to the Missouri Department of Health and Social Services surveillance system during January 1, 2008-December 31, 2019, detected geographic clusters of infection. Multilevel Poisson regression quantified small-area geographic variations and identified characteristics associated with risk for infection. Median county-level NTM infection rate was 66.33 (interquartile range 51-91)/100,000 persons. Risk of clustering was significantly higher in rural areas (rate ratio 2.82, 95% CI 1.90-4.19) and in counties with >5 floodings per year versus no flooding (rate ratio 1.38, 95% CI 1.26-1.52). Higher risk for NTM infection was associated with older age, rurality, and more flooding. Clinicians and public health professionals should be aware of increased risk for NTM infections, especially in similar environments.

Characteristics of Patients With Behçet Disease From the Van Province, Eastern Turkey: Definition of Disease Clusters in a Tertiary Referral Center.

BACKGROUND: Behçet disease (BD) is a chronic inflammatory systemic disease that affects skin mucosa, joints, eyes, and blood vessels. Behçet disease shows some clinical differences in terms of disease manifestations and prognosis among the Silk Road countries, as well as various ethnicities even in the same country. In this study, we aimed to evaluate the clinical features and disease course of BD using cluster analysis in Van province, Eastern Turkey. METHODS: This study was carried out in a tertiary referral center in Van province, by reviewing medical records. Seven disease manifestations were included to the cluster analysis as follows: mucocutaneous findings (oral ulcer, genital ulcer, erythema nodosum-like lesions, pseudofolliculitis), uveitis, superficial thrombophlebitis, musculoskeletal involvement, gastrointestinal system involvement, vascular involvement, and parenchymal central nervous system involvement. RESULTS: We identified 467 patients. After the exclusion of 6 patients who had missing data and 17 patients who did not live in Van, 444 patients (59.2% male) included into the study. Meeting the International Study Group and the International Criteria for Behçet Disease criteria were 91.6% and 96%, respectively, and 91.3% (n = 379/415) of these patients met both criteria. Four clusters were identified in the analyses: 132 patients (31.2%) in vascular (C1), 66 patients (15.6%) in ocular (C2), 60 patients (14.2%) in musculoskeletal (C3), and 165 patients (39%) in mucocutaneous (C4) clusters. Male gender ( p = 0.002; odds ratio [OR], 6.5; 95% confidence interval [CI], 2-21.4), superficial thrombophlebitis ( p = 0.001; OR, 4.7; 95% CI, 1.9-11.4), and uveitis ( p = 0.01; OR, 3.6; 95% CI, 1.3-9.9) were associated with vascular involvement in multivariate analysis. CONCLUSIONS: In our study, 4 clusters were detected in patients with BD from Van province. The prevalence of severe manifestations of BD may be related to genetic or environmental factors, such as differences in ethnicity and/or geographical differences. Despite the higher proportion of patients with a more severe disease, a favorable outcome was observed in our cohort.

Endemismo urbano por COVID-19 en Petrópolis: Detección de un foco endémico mediante análisis espacial / Urban COVID-19 endemism in Petrópolis: detection of an endemic focus by spatial analysis

RESUMEN Se realizó un estudio con el objetivo de identificar un ecosistema de endemismo urbano que explique la persistencia del SARS-CoV-2 durante los primeros 18 meses de la pandemia en el municipio de Petrópolis, Río de Janeiro, Brasil. Se analizaron los registros oficiales de casos mensuales de COVID-19, georreferenciados según el domicilio de residencia de cada caso confirmado y se elaboraron mapas de calor mensuales que identifican puntos con diferentes densidades espaciales de la enfermedad mediante la aplicación de la metodología de kernel. Se identificaron puntos de calor con cinco niveles de intensidad para la densidad espacial de casos. Los puntos de mayor intensidad, conocidos como «hotspots¼, se mantuvieron constantes durante todo el período en un polígono de aproximadamente 4 km2 ubicado en el centro de la ciudad de Petrópolis. En conclusión, se encontró que la mayor concentración de casos se mantuvo en la misma ubicación a lo largo del tiempo, a pesar de la dispersión esporádica de los casos en el territorio municipal.

ABSTRACT This study aimed to identify an ecosystem of urban endemism that explains the persistence of SARS-CoV-2 during the first 18 months of the pandemic in the municipality of Petrópolis, Rio de Janeiro, Brazil. We analyzed official records of monthly COVID-19 cases, georeferenced according to the residence address of each confirmed case. Monthly heat maps identifying points with different spatial densities of the disease were constructed by applying the kernel methodology. Heat spots with five intensity levels were identified for the spatial density of cases. The points of highest intensity, known as hotspots, remained constant throughout the period in a polygon of approximately 4 km2 located in the center of the city of Petrópolis. In conclusion, we found that the highest concentration of cases remained in the same location over time, despite the sporadic dispersion of cases within the municipal territory.

Multiomics Reveals Symbionts, Pathogens, and Tissue-Specific Microbiome of Blacklegged Ticks (Ixodes scapularis) from a Lyme Disease Hot Spot in Southeastern Ontario, Canada.

Ticks in the family Ixodidae are important vectors of zoonoses, including Lyme disease (LD), which is caused by spirochete bacteria from the Borreliella (Borrelia) burgdorferi sensu lato complex. The blacklegged tick (Ixodes scapularis) continues to expand across Canada, creating hot spots of elevated LD risk at the leading edge of its expanding range. Current efforts to understand the risk of pathogen transmission associated with I. scapularis in Canada focus primarily on targeted screens, while natural variation in the tick microbiome remains poorly understood. Using multiomics consisting of 16S metabarcoding and ribosome-depleted, whole-shotgun RNA transcriptome sequencing, we examined the microbial communities associated with adult I. scapularis (n = 32), sampled from four tissue types (whole tick, salivary glands, midgut, and viscera) and three geographical locations within a LD hot spot near Kingston, Ontario, Canada. The communities consisted of both endosymbiotic and known or potentially pathogenic microbes, including RNA viruses, bacteria, and a Babesia sp. intracellular parasite. We show that ß-diversity is significantly higher between the bacterial communities of individual tick salivary glands and midguts than that of whole ticks. Linear discriminant analysis effect size (LEfSe) determined that the three potentially pathogenic bacteria detected by V4 16S rRNA sequencing also differed among dissected tissues only, including a Borrelia strain from the B. burgdorferi sensu lato complex, Borrelia miyamotoi, and Anaplasma phagocytophilum. Importantly, we find coinfection of I. scapularis by multiple microbes, in contrast to diagnostic protocols for LD, which typically focus on infection from a single pathogen of interest (B. burgdorferi sensu stricto). IMPORTANCE As a vector of human health concern, blacklegged ticks (Ixodes scapularis) transmit pathogens that cause tick-borne diseases (TBDs), including Lyme disease (LD). Several hot spots of elevated LD risk have emerged across Canada as I. scapularis expands its range. Focusing on a hot spot in southeastern Ontario, we used high-throughput sequencing to characterize the microbiome of whole ticks and dissected salivary glands and midguts. Compared with whole ticks, salivary glands and midguts were more diverse and associated with distinct bacterial communities that are less dominated by Rickettsia endosymbiont bacteria and are enriched for pathogenic bacteria, including a B. burgdorferi sensu lato-associated Borrelia sp., Borrelia miyamotoi, and Anaplasma phagocytophilum. We also found evidence of coinfection of I. scapularis by multiple pathogens. Overall, our study highlights the challenges and opportunities associated with the surveillance of the microbiome of I. scapularis for pathogen detection using metabarcoding and metatranscriptome approaches.

Prioritizing interventions for cholera control in Kenya, 2015-2020.

Kenya has experienced cholera outbreaks since 1971, with the most recent wave beginning in late 2014. Between 2015-2020, 32 of 47 counties reported 30,431 suspected cholera cases. The Global Task Force for Cholera Control (GTFCC) developed a Global Roadmap for Ending Cholera by 2030, which emphasizes the need to target multi-sectoral interventions in priority cholera burden hotspots. This study utilizes the GTFCC's hotspot method to identify hotspots in Kenya at the county and sub-county administrative levels from 2015 through 2020. 32 of 47 (68.1%) counties reported cholera cases during this time while only 149 of 301 (49.5%) sub-counties reported cholera cases. The analysis identifies hotspots based on the mean annual incidence (MAI) over the past five-year period and cholera's persistence in the area. Applying a MAI threshold of 90th percentile and the median persistence at both the county and sub-county levels, we identified 13 high risk sub-counties from 8 counties, including the 3 high risk counties of Garissa, Tana River and Wajir. This demonstrates that several sub-counties are high level hotspots while their counties are not. In addition, when cases reported by county versus sub-county hotspot risk are compared, 1.4 million people overlapped in the areas identified as both high-risk county and high-risk sub-county. However, assuming that finer scale data is more accurate, 1.6 million high risk sub-county people would have been misclassified as medium risk with a county-level analysis. Furthermore, an additional 1.6 million people would have been classified as living in high-risk in a county-level analysis when at the sub-county level, they were medium, low or no-risk sub-counties. This results in 3.2 million people being misclassified when county level analysis is utilized rather than a more-focused sub-county level analysis. This analysis highlights the need for more localized risk analyses to target cholera intervention and prevention efforts towards the populations most vulnerable.

Molecular Epidemiology of Begomoviruses Infecting Mungbean from Yellow Mosaic Disease Hotspot Regions of India.

The major threat to mungbean (Vigna radiata L.) cultivation in the Indian subcontinent is yellow mosaic diseases (YMD), caused by Begomovirus containing bipartite genomes (DNA-A and DNA-B). In the current study, we address the epidemiology of begomoviruses infecting mungbean plants in three YMD hotspot regions of India. Full-length genomic components of the viruses from the symptomatic leaves were cloned by rolling circle amplification (RCA) and sequenced. Mungbean yellow mosaic virus (MYMV) was detected in Bihar and mungbean yellow mosaic India virus (MYMIV) in Assam and Orissa. Furthermore, we studied the population structure and genetic diversity of MYMV and MYMIV isolates of Vigna species reported to date from India. Interestingly, based on phylogenetics, we observed independent evolution of DNA-A and coevolution of DNA-B of MYMV and MYMIV. This finding is supported by the high mutation rate and recombination events in DNA-B, particularly in BV1 and BC1 genes over DNA-A, with high transition/transversion bias (R) for DNA-A over DNA-B. To investigate the effect of Begomovirus infection in plants, we constructed infectious clones (i.e. MYMV and MYMIV) and inoculated them to eight mungbean genotypes, cowpea (Vigna unguiculata L.) and tobacco (Nicotiana benthamiana) through agroinfiltration. The infected plants developed varying degrees of typical YMD symptoms. Based on the disease severity score and viral titre, mungbean genotypes were categorized as highly susceptible to MYMV (ML267) and MYMIV (K851) and immune to MYMV (PDM139, SML668) and MYMIV (Pusa Vishal). Conclusively, our findings may help prevent an epidemic of YMD in Vigna species and develop mungbean genotypes resistant to YMD via breeding programs.