Spatial and temporal variation and hotspot detection of kala-azar disease in Vaishali district (Bihar), India

Modelling spatiotemporal patterns of visceral leishmaniasis incidence in two endemic states in India using environment, bioclimatic and demographic data, 2013-2022

BACKGROUND

As of 2021, the National Kala-azar Elimination Programme (NKAEP) in India has achieved visceral leishmaniasis (VL) elimination (<1 case / 10,000 population/year per block) in 625 of the 633 endemic blocks (subdistricts) in four states. The programme needs to sustain this achievement and target interventions in the remaining blocks to achieve the WHO 2030 target of VL elimination as a public health problem. An effective tool to analyse programme data and predict/ forecast the spatial and temporal trends of VL incidence, elimination threshold, and risk of resurgence will be of use to the programme management at this juncture.

METHODOLOGY/PRINCIPAL FINDINGS

We employed spatiotemporal models incorporating environment, climatic and demographic factors as covariates to describe monthly VL cases for 8-years (2013-2020) in 491 and 27 endemic and non-endemic blocks of Bihar and Jharkhand states. We fitted 37 models of spatial, temporal, and spatiotemporal interaction random effects with covariates to monthly VL cases for 6-years (2013-2018, training data) using Bayesian inference via Integrated Nested Laplace Approximation (INLA) approach. The best-fitting model was selected based on deviance information criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) and was validated with monthly cases for 2019-2020 (test data). The model could describe observed spatial and temporal patterns of VL incidence in the two states having widely differing incidence trajectories, with >93% and 99% coverage probability (proportion of observations falling inside 95% Bayesian credible interval for the predicted number of VL cases per month) during the training and testing periods. PIT (probability integral transform) histograms confirmed consistency between prediction and observation for the test period. Forecasting for 2021-2023 showed that the annual VL incidence is likely to exceed elimination threshold in 16-18 blocks in 4 districts of Jharkhand and 33-38 blocks in 10 districts of Bihar. The risk of VL in non-endemic neighbouring blocks of both Bihar and Jharkhand are less than 0.5 during the training and test periods, and for 2021-2023, the probability that the risk greater than 1 is negligible (P<0.1). Fitted model showed that VL occurrence was positively associated with mean temperature, minimum temperature, enhanced vegetation index, precipitation, and isothermality, and negatively with maximum temperature, land surface temperature, soil moisture and population density.

CONCLUSIONS/SIGNIFICANCE

The spatiotemporal model incorporating environmental, bioclimatic, and demographic factors demonstrated that the KAMIS database of the national programmme can be used for block level predictions of long-term spatial and temporal trends in VL incidence and risk of outbreak / resurgence in endemic and non-endemic settings. The database integrated with the modelling framework and a dashboard facility can facilitate such analysis and predictions. This could aid the programme to monitor progress of VL elimination at least one-year ahead, assess risk of resurgence or outbreak in post-elimination settings, and implement timely and targeted interventions or preventive measures so that the NKAEP meet the target of achieving elimination by 2030.

Understanding the spread of infectious diseases in edge areas of hotspots: dengue epidemics in tropical metropolitan regions

Identifying clusters or hotspots from disease maps is critical in research and practice. Hotspots have been shown to have a higher potential for transmission risk and may be the source of infections, making them a priority for controlling epidemics. However, the role of edge areas of hotspots in disease transmission remains unclear. This study aims to investigate the role of edge areas in disease transmission by examining whether disease incidence rate growth is higher in the edges of disease hotspots during outbreaks. Our data is based on the three most severe dengue epidemic years in Kaohsiung city, Taiwan, from 1998 to 2020. We employed conditional autoregressive (CAR) models and Bayesian areal Wombling methods to identify significant edge areas of hotspots based on the extent of risk difference between adjacent areas. The difference-in-difference (DID) estimator in spatial panel models measures the growth rate of risk by comparing the incidence rate between two groups (hotspots and edge areas) over two time periods. Our results show that in years characterized by exceptionally large-scale outbreaks, the edge areas of hotspots have a more significant increase in disease risk than hotspots, leading to a higher risk of disease transmission and potential disease foci. This finding explains the geographic diffusion mechanism of epidemics, a pattern mixed with expansion and relocation, indicating that the edge areas play an essential role. The study highlights the importance of considering edge areas of hotspots in disease transmission. Furthermore, it provides valuable insights for policymakers and health authorities in designing effective interventions to control large-scale disease outbreaks.

Distribution and Risk of Cutaneous Leishmaniasis in Khyber Pakhtunkhwa, Pakistan

Cutaneous leishmaniasis (CL) is a zoonotic infection caused by obligate intracellular protozoa of the genus Leishmania. This study aimed to investigate CL in Khyber Pakhtunkhwa, Pakistan and to estimate the risk of epidemics. Clinico-epidemiological data of 3188 CL patients were collected from health facilities in 2021. Risk factors were analyzed using the chi-square test. ArcGIS V.10.7.1 was applied for spatial analysis. The association between CL occurrence and climatic variables was examined by Bayesian geostatistical analysis. The clinical data revealed males or individuals younger than 20 years old were more affected. Most patients presented with a single lesion, and the face was the most attacked body part. CL was prevalent in the southern region in winter. A proportional symbol map, a choropleth map, and a digital elevation model map were built to show the distribution of CL. Focal transmission was predicted by inverse distance weighting interpolation. Cluster and outlier analysis identified clusters in Bannu, Dir Lower, and Mardan, and hotspot analysis suggested Bannu as a high-risk foci. Bayesian geostatistical analysis indicated that increasing precipitation and temperature as well as low altitudes were associated with CL infection. The study has provided important information for public health sectors to develop intervention strategies for future CL epidemics.

Epidemiological features and spatial-temporal clustering of visceral leishmaniasis in mainland China from 2019 to 2021

Background

Visceral leishmaniasis (VL) is a serious vector-borne disease in central and western China. In recent years, the number of VL cases increased gradually, particularly the mountain-type zoonotic visceral leishmaniasis (MT-ZVL). This study clarified the epidemiological features and spatial-temporal clustering of VL in China between 2019 and 2021, identified the risk areas for VL transmission, and provided scientific evidence for the prevention and control of VL.

Materials and methods

The information on VL cases in 2019–2021 was collected from the Infectious Disease Reporting Information Management System of the Chinese Center for Disease Control and Prevention. The epidemiological characteristics of VL cases were analyzed. The global Moran’s I and Getis-ORD Gi* statistical data were processed for spatial autocorrelation and hotspot analysis in ESRI ArcGIS software. Also, spatial-temporal clustering analysis was conducted with the retrospective space–time permutation scan statistics.

Results

A total of 608 VL cases were reported from 2019 to 2021, with 158, 213, and 237 cases reported each year, respectively. Of the 608 cases, there were 10 cases of anthroponotic visceral leishmaniasis (AVL), 20 cases of desert-type zoonotic visceral leishmaniasis (DT-ZVL), and 578 cases of MT-ZVL. The age of VL cases was mainly distributed in the group of subjects aged ≥ 15 years. Peasants and infants were the dominant high-risk population. The incidence peak season of VL occurred between March and May. The cases were mainly distributed in Shanxi (299 cases), Shaanxi (118 cases), and Gansu (106 cases) Provinces, accounting for 86.02% (523/608) of the total reported cases in China. Spatial analysis revealed that clustering of infection is mainly located in eastern Shanxi Province and Shaanxi–Shanxi border areas, as well as southern Gansu and northern Sichuan Province. In addition, new reemergence hotspots in Shanxi, Henan, and Hebei Provinces have been detected since 2020. Spatio-temporal clustering analysis revealed an increase in the degree of infection aggregation in eastern Shanxi Province and Shaanxi–Shanxi border areas.

Conclusion

The AVL and DT-ZVL were endemic at a lower level in western China, whereas MT-ZVL rebounded rapidly and showed a resurgence in historically endemic counties. The spatial-temporal clustering analysis displayed that the high-incidence areas of VL have shifted to central China, particularly in Shanxi and Shaanxi Provinces. Integrated mitigation strategies targeting high-risk populations are needed to control VL transmission in high-risk areas.

Integrating Spatial Modelling and Space-Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan

The spatial–temporal assessment of vector diseases is imperative to design effective action plans and establish preventive strategies. Therefore, such assessments have potential public health planning-related implications. In this context, we here propose an integrated spatial disease evaluation (I-SpaDE) framework. The I-SpaDE integrates various techniques such as the Kernel Density Estimation, the Optimized Hot Spot Analysis, space–time assessment and prediction, and the Geographically Weighted Regression (GWR). It makes it possible to systematically assess the disease concentrations, patterns/trends, clustering, prediction dynamics, and spatially varying relationships between disease and different associated factors. To demonstrate the applicability and effectiveness of the I-SpaDE, we apply it in the second largest city of Pakistan, namely Lahore, using Dengue Fever (DF) during 2007–2016 as an example vector disease. The most significant clustering is evident during the years 2007–2008, 2010–2011, 2013, and 2016. Mostly, the clusters are found within the city’s central functional area. The prediction analysis shows an inclination of DF distribution from less to more urbanized areas. The results from the GWR show that among various socio-ecological factors, the temperature is the most significantly associated with the DF followed by vegetation and built-up area. While the results are important to understand the DF situation in the study area and have useful implications for public health planning, the proposed framework is flexible, replicable, and robust to be utilized in other similar regions, particularly in developing countries in the tropics and sub-tropics.

Transient Ischemic Attack (TIA) Incidence with Geographic Information Systems (GIS) Mapping for Stroke Prevention Interventions

Objectives:

GIS mapping as a public health tool has been increasingly applied to chronic disease control. While evaluating TIA incidence from an existing regional stroke registry in Ludhiana city, India, we aim to apply the innovative concept of regional TIA GIS mapping for planning targeted stroke prevention interventions.

Methods:

TIA patient data was obtained from hospitals, scan centers and general practitioners from March 2010 to March 2013 using WHO-Stroke STEPS based surveillance as part of establishing a population-based stroke registry in Ludhiana city. From this registry, patients with TIA (diagnosed by MRI image-based stroke rule-out, or clinically) were chosen and data analyzed.

Results:

A total of 138 TIA patients were included in the final analysis. The annual TIA incidence rate for Ludhiana city was 7.13/100,000 (95% confidence interval: 5.52 to 8.74) for 2012-2013. Mean age was 58.5 ± 13.9 years (range: 22-88 years) and 87 (63%) were men. Majority of the TIA cases had anterior circulation TIAs. Hypertension (87.4%) was the most common risk factor. Using Geographic Information System (GIS) mapping, high TIA incidence was seen in central, western, and southern parts and clustering of TIA cumulative incidence was seen in the central part of Ludhiana city.

Conclusion:

Incidence rate of TIA was lower than that expected from a low- and middle-income country (LMIC). TIA GIS mapping, looking at regional localization, can be a novel option for developing targeted, cost-effective stroke prevention programs.

Improved kala-azar case management through implementation of health facility-based sentinel sites surveillance system in Bihar, India

Background

Visceral leishmaniasis (VL), also known as kala-azar (KA), is a neglected vector-borne disease, targeted for elimination, but several affected blocks of Bihar are posing challenges with the high incidence of cases, and moreover, the disease is spreading in newer areas. High-quality kala-azar surveillance in India, always pose great concern. The complete and accurate patient level data is critical for the current kala-azar management information system (KMIS). On the other side, no accurate data on the burden of post kala-azar dermal leishmaniasis (PKDL) and co-infections are available under the current surveillance system, which might emerge as a serious concern. Additionally, in low case scenario, sentinel surveillance may be useful in addressing post-elimination activities and sustaining kala-azar (KA) elimination. Health facility-based sentinel site surveillance system has been proposed, first time to do a proper accounting of KA, PKDL and co-infection morbidity, mortality, diagnosis, case management, hotspot identification and monitoring the impact of elimination interventions.

Methodology/principal findings

Kala-azar sentinel site surveillance was established and activated in thirteen health facilities of Bihar, India, using stratified sampling technique during 2011 to 2014. Data were collected through specially designed performa from all patients attending the outpatient departments of sentinel sites. Among 20968 symptomatic cases attended sentinel sites, 2996 cases of KA and 53 cases of PKDL were registered from 889 endemic villages. Symptomatic cases meant a person with fever of more than 15 days, weight loss, fatigue, anemia, and substantial swelling of the liver and spleen (enlargement of spleen and liver).The proportion of new and old cases was 86.1% and 13.9% respectively. A statistically significant difference was observed for reduction in KA incidence from 4.13/10000 in 2011 to 1.75/10000 in 2014 (p<0.001). There were significant increase (0.08, 0.10 per 10 000 population) in the incidences of PKDL and co-infection respectively in the year 2014 as compared to that of 2011 (0.03, 0.06 per 10 000 population). The proportion of HIV-VL co-infection was significantly higher (1.6%; p<0.05) as compared to other co-infections. Proportions of male in all age groups were higher and found statistically significant (Chi-square test = 7.6; P = 0.026). Utilization of laboratory services was greatly improved. Friedman test showed statistically significant difference between response of different anti kala-azar drugs (F = 25.0, P = 0.004).The initial and final cure rate of AmBisome was found excellent (100%). The results of the signed rank sum test showed significant symmetry of unresponsiveness rate (P = 0.03). Similarly, relapse rate of sodium antimony gluconate (SAG) was also found significantly higher as compared to other drugs (95%CI 0.2165 to 19.7035; P = 0.03). A statistically significant difference was found (p<0.001) between villages having 1–2 cases (74%) and villages with 3–5 cases (15%). Significantly higher proportion (95%) of cases were captured by existing Govt. surveillance system (KMIS) (p<0.001), as compared to private providers (5%).

Conclusions/significance

Establishment of a sentinel site based kala-azar surveillance system in Bihar, India effectively detected the rising trend of PKDL and co-infections and captured complete and accurate patient level data. Further, this system may provide a model for improving laboratory services, KA, PKDL and co-infection case management in other health facilities of Bihar without further referral. Program managers may use these results for evaluating program’s effectiveness. It may provide an example for changing the practices of health care workers in Bihar and set a benchmark of high quality surveillance data in a resource limited setting. However, the generalizability of this sentinel surveillance finding to other context remains a major limitation of this study. The justifications for this; the sentinel sites were made in the traditionally high endemic PHC’s. The other conditions were Program commitment for diagnostic (rk-39) and the first line anti kala-azar drug i.e. miltefosine throughout the study period in the sentinel sites. In addition, there were clause of fulfillment of readiness criteria at each sentinel site (already described in the line no 171 to 180 at page no-8, 181–189 at page no-9 and 192–212 at page no-10). Rigorous efforts were taken to improve all the sentinel sites to meet the readiness criteria and research activities started only after meeting readiness criteria at the site. Therefore sentinel site surveillance described under the present study cannot be integrated into other set up (medium and low endemic areas). However, it can be integrated into highly endemic areas with program commitment and fulfillment of readiness criteria.

Visceral leishmaniasis is a neglected vector-borne disease, and one of the major public health problems of Bihar. The disease has been targeted for elimination by 2020. Bihar still posing challenge i.e. incidence is much high in a number of affected blocks, and even the disease is spreading in newer areas. None availability of an accurate data on the burden of post kala-azar dermal leishmaniasis (PKDL) and co-infections under the current surveillance system may emerge as a serious concern. Therefore, health facility-based sentinel site surveillance system has been attempted for the first time in Bihar for proper accounting of KA, PKDL and co-infection morbidity, mortality, diagnosis, case management, hotspot identification and monitoring the impact of elimination interventions. A system for capturing regional transmission is essential that can target focal areas of infection to monitor progress towards kala-azar elimination. Kala-azar sentinel site surveillance was established and activated in thirteen health facilities of Bihar during 2011 to 2014. The results showed a significant increase in PKDL and co-infection in 2014 when compared to 2011. Findings further revealed that utilization of laboratory services and case management were greatly improved, as majority of patients with KA, PKDL & co-infections were managed by the sentinel sites itself. The final cure rate of AmBisome was found excellent (100%). These observations may be useful to provide the basis for the design, refinement and resource allocation of the kala-azar control program. This system may also be useful in measuring impact of elimination interventions, their effectiveness and finally help in improving program management. It may further be used as an example for changing the practices of health care workers in Bihar and a lesson how to achieve high quality surveillance data in a resource limited setting. Standardization of sentinel site surveillance in terms of improved procedure, training, logistics, etc may further increase the effectiveness of this system. It may possibly be used as a cost-effective approach for capturing real-time kala-azar data under national kala-azar elimination programme.

A geospatial based hotspot and regression analysis of abortion and stillbirth prevalence in Manipur, India

Safe delivery of healthy newborn from the state of pregnancy is one of the main objectives of prenatal health care. Women face maternal health issues due to lack of awareness, excessive stress during pregnancies and lack of appropriate maternal health-care services. The main objectives of this research study is to identify the abortion and stillbirth prevalent hotspot zones in Manipur from the year 2011 to 2018 and identification of statistically significant factors related to the cause of abortion and stillbirth. It was observed from the hotspot results that Lamphelpat, Kakching, Thoubal, and Churachandpur are the blocks where abortion is more prevalent, whereas stillbirth cases were found to be concentrated in Lamphelpat block. Generated regression model showed good model performance with adjusted R² = 0.68, Akaike Information Criterion (AIC) = 608.72, Moran's I = 0.37, and adjusted R² = 0.73, AIC = 433.26, Moran's I = -0.05 for abortion and stillbirth, respectively. Through regression modeling three factors i.e., main worker female, female working as agriculture labor and female household worker were found to be significant for the cause maternal issues like abortion and stillbirth. There is a need to follow up pregnant women with these risk factors more frequently and in a more qualified way.

Using geographically weighted regression analysis to cluster under-nutrition and its predictors among under-five children in Ethiopia: Evidence from demographic and health survey

BACKGROUND

Malnutrition among under-five children is a common public health problem and it is one of the main cause for the mortality of under-five children in developing countries, including Ethiopia. Therefore, lack of evidence about geographic heterogeneity and predictors of under-nutrition hinders for evidence-based decision-making process for the prevention and control programs of under-nutrition in Ethiopia. Thus, this study aimed to address this gap.

METHODS

The data were obtained from the Ethiopian Demographic and Health Survey (EDHS) 2016. A total of 9,384 under-five children nested in 645 clusters were included with a stratified two-stage cluster sampling. ArcGIS version 10.5 software was used for global, local and ordinary least square analysis and mapping. The spatial autocorrelation (Global Moran's I) statistic was held in order to assess the pattern of wasting, stunting, and underweight whether it was dispersed, clustered, or randomly distributed. In addition, a Bernoulli model was used to analyze the purely spatial cluster detection of under-nutrition indicators through SaTScan version 9.6 software. Geographically weighted regression (GWR) version 4.0 software was used to model spatial relationships in the GWR analysis. Finally, a statistical decision was made at p-value<0.05 with 95%CI for ordinary least square analysis and geographically weighted regression.

MAIN FINDINGS

Childhood under-nutrition showed geographical variations at zonal levels in Ethiopia. Accordingly, Somali region (Afder, Gode, Korahe, Warder Zones), Afar region (Zone 2), Tigray region (Southern Zone), and Amhara region (Waghmira Zones) for wasting, Amhara region (West Gojam, Awi, South Gondar, and Waghmira Zones) for stunting and Amhara region (South Wollo, North Wollo, Awi, South Gondar, and Waghmira zones), Afar region (Zone 2), Tigray region (Eastern Zone, North Western Zone, Central Zone, Southern Zone, and Mekele Special Zones), and Benshangul region (Metekel and Assosa Zones) for underweight were detected as hot spot (high risk) regions. In GWR analysis, had unimproved toilet facility for stunting, wasting and underweight, father had primary education for stunting and wasting, father had secondary education for stunting and underweight, mothers age 35-49 years for wasting and underweight, having female children for stunting, having children eight and above for wasting, and mother had primary education for underweight were significant predictors at (p<0.001).

CONCLUSIONS

Our study showed that the spatial distribution of under-nutrition was clustered and high-risk areas were identified in all forms of under-nutrition indicators. Predictors of under-nutrition were identified in all forms of under-nutrition indicators. Thus, geographic-based nutritional interventions mainly mobilizing additional resources could be held to reduce the burden of childhood under-nutrition in hot spot areas. In addition, improving sanitation and hygiene practice, improving the life style of the community, and promotion of parent education in the identified hot spot zones for under-nutrition should be more emphasized.

Spatiotemporal patterns of cutaneous leishmaniasis in the district upper and lower Dir, Khyber Pakhtunkhwa, Pakistan: A GIS-based spatial approaches

While Cutaneous leishmaniasis (CL) is not a life-threatening disease, it leads to devastating effects on local community. CL is widely scattered manifesting a noticeable epidemiological pattern around the globe. The present study was planned to address the role of Geographic Information System (GIS) using CL clinico-epidemiological data to determine the high-risk areas of CL. Recorded data (2014-2018) of 3630 positive individuals was collected from Basic Health Units of the Upper and Lower Dir Districts, Khyber Pakhtunkhwa, Pakistan. Descriptive and statistical analysis was used for clinico-epidemiological characterization. For spatial analysis, ArcGIS V.10.3 was used and the CL average incidence was tagged on the proportional, choropleth, and digital elevation model maps. For focal transmission and high-risk zones, Inverse Density Weight (IDW) spatial interpolation, focal statistics, hot spot, cluster and outlier, and Bayesian geostatistical analysis were used. The trend of CL cases was elevated from 2014 to 2016 except for 2017 and 2018. Individuals of both genders younger than 20 years old were highly susceptible. Single lesions were more prominent (56%) and frequently affected facial parts (51%). The size and pretreatment duration of the CL lesion was significantly associated. Spatially, a choropleth map displayed the maximum CL incidences in Tehsil Balambat, Khal, and Termergara (31%-13%) located within a range of 948-1947m elevation in the central regions with proximal CL transmissions. Hot spot and cluster and outlier analysis affirmed that Tehsil Khal was the high-risk CL foci. The Bayesian geostatistical analysis revealed high temperature, less altitude, and annual precipitation as important risk factors. An increasing trend in CL transmission has become evident, affecting both genders and <20 years old children. GIS resolute the concealed CL hubs in the least elevated central regions which warrant the control strategies to restrict future epidemics.

Spatiotemporal distribution of cutaneous leishmaniasis in Sri Lanka and future case burden estimates

BACKGROUND

Leishmaniasis is a neglected tropical vector-borne disease, which is on the rise in Sri Lanka. Spatiotemporal and risk factor analyses are useful for understanding transmission dynamics, spatial clustering and predicting future disease distribution and trends to facilitate effective infection control.

METHODS

The nationwide clinically confirmed cutaneous leishmaniasis and climatic data were collected from 2001 to 2019. Hierarchical clustering and spatiotemporal cross-correlation analysis were used to measure the region-wide and local (between neighboring districts) synchrony of transmission. A mixed spatiotemporal regression-autoregression model was built to study the effects of climatic, neighboring-district dispersal, and infection carryover variables on leishmaniasis dynamics and spatial distribution. Same model without climatic variables was used to predict the future distribution and trends of leishmaniasis cases in Sri Lanka.

RESULTS

A total of 19,361 clinically confirmed leishmaniasis cases have been reported in Sri Lanka from 2001-2019. There were three phases identified: low-transmission phase (2001-2010), parasite population buildup phase (2011-2017), and outbreak phase (2018-2019). Spatially, the districts were divided into three groups based on similarity in temporal dynamics. The global mean correlation among district incidence dynamics was 0.30 (95% CI 0.25-0.35), and the localized mean correlation between neighboring districts was 0.58 (95% CI 0.42-0.73). Risk analysis for the seven districts with the highest incidence rates indicated that precipitation, neighboring-district effect, and infection carryover effect exhibited significant correlation with district-level incidence dynamics. Model-predicted incidence dynamics and case distribution matched well with observed results, except for the outbreak in 2018. The model-predicted 2020 case number is about 5,400 cases, with intensified transmission and expansion of high-transmission area. The predicted case number will be 9115 in 2022 and 19212 in 2025.

CONCLUSIONS

The drastic upsurge in leishmaniasis cases in Sri Lanka in the last few year was unprecedented and it was strongly linked to precipitation, high burden of localized infections and inter-district dispersal. Targeted interventions are urgently needed to arrest an uncontrollable disease spread.

Spatio-temporal clustering of Mountain-type Zoonotic Visceral Leishmaniasis in China between 2015 and 2019

With several decades of concerted control efforts, visceral leishmaniasis(VL) eradication had almost been achieved in China. However, VL cases continue to be detected in parts of western China recent years. Using data of reported cases, this study aimed to investigate the epidemiology and spatio⁃temporal distribution, of mountain-type zoonotic visceral leishmaniasis (MT-ZVL) in China between the years 2015 and 2019. Epidemiological data pertaining to patients with visceral leishmaniasis (VL) were collected in Gansu, Shaanxi, Sichuan, Shanxi, Henan and Hebei provinces between the years 2015 and 2019. Joinpoint regression analysis was performed to determine changes in the epidemic trend of MT-ZVL within the time period during which data was collected. Spatial autocorrelation of infection was examined using the Global Moran's I statistic wand hotspot analysis was carried out using the Getis-Ord Gi* statistic. Spatio-temporal clustering analysis was conducted using the retrospective space-time permutation flexible spatial scanning statistics. A total of 529 cases of MT-ZVL were detected in the six provinces from which data were collected during the study time period, predominantly in Gansu (55.0%), Shanxi (21.7%), Shaanxi (12.5%) and Sichuan (8.9%) provinces. A decline in VL incidence in China was observed during the study period, whereas an increase in MT-ZVL incidence was observed in the six provinces from which data was obtained (t = 4.87, P < 0.05), with highest incidence in Shanxi province (t = 16.91, P < 0.05). Significant differences in the Moran's I statistic were observed during study time period (P < 0.05), indicating spatial autocorrelation in the spatial distribution of MT-ZVL. Hotspot and spatial autocorrelation analysis revealed clustering of infection cases in the Shaanxi-Shanxi border areas and in east of Shanxi province, where transmission increased rapidly over the study duration, as well as in well know high transmission areas in the south of Gansu province and the north of the Sichuan province. It indicates resurgence of MT-ZVL transmission over the latter three years of the study. Spatial clustering of infection was observed in localized areas, as well as sporadic outbreaks of infection.

Predicted distribution of sand fly (Diptera: Psychodidae) species involved in the transmission of Leishmaniasis in São Paulo state, Brazil, utilizing maximum entropy ecological niche modeling

Leishmaniasis is a public health problem worldwide. We aimed to predict ecological niche models (ENMs) for visceral (VL) and cutaneous (CL) leishmaniasis and the sand flies involved in the transmission of leishmaniasis in São Paulo, Brazil. Phlebotomine sand flies were collected between 1985 and 2015. ENMs were created for each sand fly species using Maximum Entropy Species Distribution Modeling software, and 20 climatic variables were determined. Nyssomyia intermedia (Lutz & Neiva, 1912) and Lutzomyia longipalpis (Lutz & Neiva, 1912), the primary vectors involved in CL and VL, displayed the highest suitability across the various regions, climates, and topographies. L. longipalpis was found in the border of Paraná an area currently free of VL. The variables with the greatest impact were temperature seasonality, precipitation, and altitude. Co-presence of multiple sand fly species was observed in the cuestas and coastal areas along the border of Paraná and in the western basalt areas along the border of Mato Grosso do Sul. Human CL and VL were found in 475 of 546 (86.7%) and 106 of 645 (16.4%) of municipalities, respectively. Niche overlap between N. intermedia and L. longipalpis was found with 9208 human cases of CL and 2952 cases of VL. ENMs demonstrated that each phlebotomine sand fly species has a unique geographic distribution pattern, and the occurrence of the primary vectors of CL and VL overlapped. These data can be used by public authorities to monitor the dispersion and expansion of CL and VL vectors in São Paulo state.

An assessment of risk factors for contracting rabies among dog bite cases recorded in Ward 30, Murewa district, Zimbabwe

BACKGROUND

Zoonoses are a major threat to human health. Worldwide, rabies is responsible for approximately 59 000 deaths annually. In Zimbabwe, rabies is one of the top 5 priority diseases and it is notifiable. It is estimated that rabies causes 410 human deaths per year in the country. Murewa district recorded 938 dog bite cases and 4suspected rabies deaths between January 2017 and July 2018, overshooting the threshold of zero rabies cases. Of the 938dog bite cases reported in the district, 263 were reported in Ward 30 and these included all the 4suspected rabies deaths reported in the district. This necessitated a study to assess risk factors for contracting rabies in Ward 30, Murewa.

METHODOLOGY/ PRINCIPAL FINDINGS

A descriptive cross sectional survey was used for a retrospective analysis of a group of dog bite cases reported at Murewa Hospital, in Ward 30. Purposive sampling was used to select dog bite cases and snowball sampling was used to locate unvaccinated dogs and areas with jackal presence. The dog bite cases and relatives of rabies cases were interviewed using a piloted interviewer-administered questionnaire. Geographical Positioning System (GPS) coordinates of dog bite cases, vaccinated and unvaccinated dogs and jackal presence were collected using handheld GPS device. QGIS software was used to spatially analyse and map them. Dog owners were 10 times more likely to contract rabies compared to non-dog owners (RR = 10, 95% CI 1.06-93.7). Owners of unvaccinated dogs were 5 times more likely to contract rabies compared to owners of vaccinated dogs (RR = 5.01, 95% CI 0.53-47.31). Residents of the high density cluster (area with low cost houses and stand size of 300 square meters and below) were 64 times more likely to contract rabies compared to non-high density cluster residents (RR = 64.87, 95% CI 3.6039-1167.82). Participants who were not knowledgeable were 0.07 times more likely to contract rabies, compared to those who had knowledge about rabies. (RR = 0.07, 95% CI 0.004-1.25). Our study shows that the risk factors for contacting rabies included; low knowledge levels regarding rabies, dog ownership residing in the high density cluster, owning unvaccinated dogs and spatial overlap of jackal presence with unvaccinated dogs.

Spatiotemporal Distribution of Zika Virus and Its Spatially Heterogeneous Relationship with the Environment

Infectious diseases have caused some of the most feared plagues and greatly harmed human health. However, despite the qualitative understanding that the occurrence and diffusion of infectious disease is related to the environment, the quantitative relations are unknown for many diseases. Zika virus (ZIKV) is a mosquito-borne virus that poses a fatal threat and has spread explosively throughout the world, impacting human health. From a geographical perspective, this study aims to understand the global hotspots of ZIKV as well as the spatially heterogeneous relationship between ZIKV and environmental factors using exploratory special data analysis (ESDA) model. A geographically weighted regression (GWR) model was used to analyze the influence of the dominant environmental factors on the spread of ZIKV at the continental scale. The results indicated that ZIKV transmission had obvious regional and seasonal heterogeneity. Population density, GDP per capita, and landscape fragmentation were the dominant environmental factors affecting the spread of ZIKV, which indicates that social factors had a greater influence than natural factors on the spread of it. As SARS-CoV-2 is spreading globally, this study can provide methodological reference for fighting against the pandemic.

Geospatial dynamics of COVID-19 clusters and hotspots in Bangladesh

The coronavirus disease 2019 (COVID-19) is an emerging and rapidly evolving profound pandemic, which causes severe acute respiratory syndrome and results in significant case fatality around the world including Bangladesh. We conducted this study to assess how COVID-19 cases clustered across districts in Bangladesh and whether the pattern and duration of clusters changed following the country's containment strategy using Geographic information system (GIS) software. We calculated the epidemiological measures including incidence, case fatality rate (CFR) and spatiotemporal pattern of COVID-19. We used inverse distance weighting (IDW), Geographically weighted regression (GWR), Moran's I and Getis-Ord Gi* statistics for prediction, spatial autocorrelation and hotspot identification. We used retrospective space-time scan statistic to analyse clusters of COVID-19 cases. COVID-19 has a CFR of 1.4%. Over 50% of cases were reported among young adults (21-40 years age). The incidence varies from 0.03 - 0.95 at the end of March to 15.59-308.62 per 100,000, at the end of July. Global Moran's Index indicates a robust spatial autocorrelation of COVID-19 cases. Local Moran's I analysis stated a distinct High-High (HH) clustering of COVID-19 cases among Dhaka, Gazipur and Narayanganj districts. Twelve statistically significant high rated clusters were identified by space-time scan statistics using a discrete Poisson model. IDW predicted the cases at the undetermined area, and GWR showed a strong relationship between population density and case frequency, which was further established with Moran's I (0.734; p ≤ 0.01). Dhaka and its surrounding six districts were identified as the significant hotspot whereas Chattogram was an extended infected area, indicating the gradual spread of the virus to peripheral districts. This study provides novel insights into the geostatistical analysis of COVID-19 clusters and hotspots that might assist the policy planner to predict the spatiotemporal transmission dynamics and formulate imperative control strategies of SARS-CoV-2 in Bangladesh. The geospatial modeling tools can be used to prevent and control future epidemics and pandemics.

Spatial prediction and mapping of the COVID-19 hotspot in India using geostatistical technique

The world has now facing a health crisis due to outbreak of novel coronavirus 2019 (COVID-19). The numbers of infection and death have been rapidly increasing which result in a serious threat to the social and economic crisis. India as the second most populous nation of the world has also running with a serious health crisis, where more than 8,300,500 people have been infected and 123,500 deaths due to this deadly pandemic. Therefore, it is urgent to highlight the spatial vulnerability to identify the area under risk. Taking India as a study area, a geospatial analysis was conducted to identify the hotspot areas of the COVID-19. In the present study, four factors naming total population, population density, foreign tourist arrivals to India and reported confirmed cases of the COVID-19 were taken as responsible factors for detecting hotspot of the novel coronavirus. The result of spatial autocorrelation showed that all four factors considered for hotspot analysis were clustered and the results were statistically significant (p value < 0.01). The result of Getis-Ord Gi* statistics revealed that the total population and reported COVID-19 cases have got high priority for considering hotspot with greater z-score (> 3 and > 0.7295 respectively). The present analysis reveals that the reported cases of COVID-19 are higher in Maharashtra, followed by Tamil Nadu, Gujarat, Delhi, Uttar Pradesh, and West Bengal. The spatial result and geospatial methodology adopted for detecting COVID-19 hotspot in the Indian subcontinent can help implement strategies both at the macro and micro level. In this regard, social distancing, avoiding social meet, staying at home, avoiding public transport, self-quarantine and isolation are suggested in hotspot zones; together with, the international support is also required in the country to work jointly for mitigating the spread of COVID-19.

Spatial and genetic clustering of Plasmodium falciparum and Plasmodium vivax infections in a low-transmission area of Ethiopia

The distribution of malaria infections is heterogeneous in space and time, especially in low transmission settings. Understanding this clustering may allow identification and targeting of pockets of transmission. In Adama district, Ethiopia, Plasmodium falciparum and P. vivax malaria patients and controls were examined, together with household members and immediate neighbors. Rapid diagnostic test and quantitative PCR (qPCR) were used for the detection of infections that were genetically characterized by a panel of microsatellite loci for P. falciparum (26) and P. vivax (11), respectively. Individuals living in households of clinical P. falciparum patients were more likely to have qPCR detected P. falciparum infections (22.0%, 9/41) compared to individuals in control households (8.7%, 37/426; odds ratio, 2.9; 95% confidence interval, 1.3–6.4; P = .007). Genetically related P. falciparum, but not P. vivax infections showed strong clustering within households. Genotyping revealed a marked temporal cluster of P. falciparum infections, almost exclusively comprised of clinical cases. These findings uncover previously unappreciated transmission dynamics and support a rational approach to reactive case detection strategies for P. falciparum in Ethiopia.

A multimethod approach for county-scale geospatial analysis of emerging infectious diseases: a cross-sectional case study of COVID-19 incidence in Germany

Background

As of 13 July 2020, 12.9 million COVID-19 cases have been reported worldwide. Prior studies have demonstrated that local socioeconomic and built environment characteristics may significantly contribute to viral transmission and incidence rates, thereby accounting for some of the spatial variation observed. Due to uncertainties, non-linearities, and multiple interaction effects observed in the associations between COVID-19 incidence and socioeconomic, infrastructural, and built environment characteristics, we present a structured multimethod approach for analysing cross-sectional incidence data within in an Exploratory Spatial Data Analysis (ESDA) framework at the NUTS3 (county) scale.

Methods

By sequentially conducting a geospatial analysis, an heuristic geographical interpretation, a Bayesian machine learning analysis, and parameterising a Generalised Additive Model (GAM), we assessed associations between incidence rates and 368 independent variables describing geographical patterns, socioeconomic risk factors, infrastructure, and features of the build environment. A spatial trend analysis and Local Indicators of Spatial Autocorrelation were used to characterise the geography of age-adjusted COVID-19 incidence rates across Germany, followed by iterative modelling using Bayesian Additive Regression Trees (BART) to identify and measure candidate explanatory variables. Partial dependence plots were derived to quantify and contextualise BART model results, followed by the parameterisation of a GAM to assess correlations.

Results

A strong south-to-north gradient of COVID-19 incidence was identified, facilitating an empirical classification of the study area into two epidemic subregions. All preliminary and final models indicated that location, densities of the built environment, and socioeconomic variables were important predictors of incidence rates in Germany. The top ten predictor variables’ partial dependence exhibited multiple non-linearities in the relationships between key predictor variables and COVID-19 incidence rates. The BART, partial dependence, and GAM results indicate that the strongest predictors of COVID-19 incidence at the county scale were related to community interconnectedness, geographical location, transportation infrastructure, and labour market structure.

Conclusions

The multimethod ESDA approach provided unique insights into spatial and aspatial non-stationarities of COVID-19 incidence in Germany. BART and GAM modelling indicated that geographical configuration, built environment densities, socioeconomic characteristics, and infrastructure all exhibit associations with COVID-19 incidence in Germany when assessed at the county scale. The results suggest that measures to implement social distancing and reduce unnecessary travel may be important methods for reducing contagion, and the authors call for further research to investigate the observed associations to inform prevention and control policy.

A spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in India

Background

The elimination programme for visceral leishmaniasis (VL) in India has seen great progress, with total cases decreasing by over 80% since 2010 and many blocks now reporting zero cases from year to year. Prompt diagnosis and treatment is critical to continue progress and avoid epidemics in the increasingly susceptible population. Short-term forecasts could be used to highlight anomalies in incidence and support health service logistics. The model which best fits the data is not necessarily most useful for prediction, yet little empirical work has been done to investigate the balance between fit and predictive performance.

Methodology/Principal findings

We developed statistical models of monthly VL case counts at block level. By evaluating a set of randomly-generated models, we found that fit and one-month-ahead prediction were strongly correlated and that rolling updates to model parameters as data accrued were not crucial for accurate prediction. The final model incorporated auto-regression over four months, spatial correlation between neighbouring blocks, and seasonality. Ninety-four percent of 10-90% prediction intervals from this model captured the observed count during a 24-month test period. Comparison of one-, three- and four-month-ahead predictions from the final model fit demonstrated that a longer time horizon yielded only a small sacrifice in predictive power for the vast majority of blocks.

Conclusions/Significance

The model developed is informed by routinely-collected surveillance data as it accumulates, and predictions are sufficiently accurate and precise to be useful. Such forecasts could, for example, be used to guide stock requirements for rapid diagnostic tests and drugs. More comprehensive data on factors thought to influence geographic variation in VL burden could be incorporated, and might better explain the heterogeneity between blocks and improve uniformity of predictive performance. Integration of the approach in the management of the VL programme would be an important step to ensuring continued successful control.

This paper demonstrates a statistical modelling approach for forecasting of monthly visceral leishmaniasis (VL) incidence at block level in India, which could be used to tailor control efforts according to local estimates and monitor deviations from the currently decreasing trend. By fitting a variety of models to four years of historical data and assessing predictions within a further 24-month test period, we found that the model which best fit the observed data also showed the best predictive performance, and predictive accuracy was maintained when making rolling predictions up to four months ahead of the observed data. Since there is a two-month delay between reporting and processing of the data, predictive power more than three months ahead of current data is crucial to make forecasts which can feasibly be acted upon. Some heterogeneity remains in predictive power across the study region which could potentially be improved using unit-specific data on factors believed to be associated with reported VL incidence (e.g. age distribution, socio-economic status and climate).

Intensely clustered outbreak of visceral leishmaniasis (kala-azar) in a setting of seasonal migration in a village of Bihar, India

Background

A visceral leishmaniasis outbreak was reported from a village in a low-endemic district of Bihar, India.

Methods

Outbreak investigation with house-to-house search and rapid test of kala-azar suspects and contacts was carried out. Sandfly collection and cone bio-assay was done as part of entomological study.

Results

A spatially and temporally clustered kala-azar outbreak was found at Kosra village in Sheikhpura district with 70 cases reported till December 2018. Delay of more than a year was found between diagnosis and treatment of the index case. The southern hamlet with socio-economically disadvantaged migrant population was several times more affected than rest of the village (attack rate of 19.0% vs 0.5% respectively, OR_MH = 39.2, 95% CI 18.2–84.4). The median durations between onset of fever to first contact with any health services, onset to kala-azar diagnosis, diagnosis to treatment were 10 days (IQR 4–18), 30 days (IQR 17–73) and 1 day (IQR 0.5 to 3), respectively, for 50 kala-azar cases assessed till June 2017. Three-fourths of these kala-azar cases had out-of-pocket medical expenditure for their condition. Known risk factors for kala-azar such as illiteracy, poverty, belonging to socially disadvantaged community, migration, residing in kutcha houses, sleeping in rooms with unplastered walls and non-use of mosquito nets were present in majority of these cases. Only half the dwellings of the kala-azar cases were fully sprayed. Fully gravid female P. argentipes collected post indoor residual spraying (IRS) and low sandfly mortality on cone-bioassay indicated poor effectiveness of vector control.

Conclusions

There is need to focus on low-endemic areas of kala-azar. The elimination programme should implement a routine framework for kala-azar outbreak response. Complete case-finding, use of quality-compliant insecticide and coverage of all sprayable surfaces in IRS could help interrupt transmission during outbreaks.

Review of Geospatial Technology for Infectious Disease Surveillance: Use Case on COVID-19

This paper discusses on the increasing relevancy of geospatial technologies such as geographic information system (GIS) in the public health domain, particularly for the infectious disease surveillance and modelling strategies. Traditionally, the disease mapping tasks have faced many challenges—(1) authors rarely documented the evidence that were used to create map, (2) before evolution of GIS, many errors aroused in mapping tasks which were expanded extremely at global scales, and (3) there were no fidelity assessment of maps which resulted in inaccurate precision. This study on infectious diseases geo-surveillance is divided into four broad sections with emphasis on handling geographical and temporal issues to help in public health decision-making and planning policies: (1) geospatial mapping of diseases using its spatial and temporal information to understand their behaviour across geography; (2) the citizen’s involvement as volunteers in giving health and disease data to assess the critical situation for disease’s spread and prevention in neighbourhood effect; (3) scientific analysis of health-related behaviour using mathematical epidemiological and geo-statistical approaches with (4) capacity building program. To illustrate each theme, recent case studies are cited and case studies are performed on COVID-19 to demonstrate selected models.

Risk mapping of visceral leishmaniasis in Brazil

INTRODUCTION

Brazil experiences a large number of visceral leishmaniasis (VL) cases. Our objective was to examine both spatial patterns of dispersion and space-time trends for this disease.

METHODS

We used all autochthonous confirmed cases of VL in Brazil from 2001 to 2017.

RESULTS

Throughout Brazil, 53,715 human cases of VL were recorded. The Northeast, Southeast, and Midwest regions of Brazil were the most affected areas and presented a higher risk of transmission. Regarding spatiotemporal variation, significant differences were observed each year, with a peak in 2005.

CONCLUSIONS

The dynamics of VL showed a clear non-random pattern of spread in Brazil.

Detecting spatio-temporal hotspots of scarlet fever in Taiwan with spatio-temporal Gi* statistic

A resurgence of scarlet fever has caused many pediatric infections in East Asia and the United Kingdom. Although scarlet fever in Taiwan has not been a notifiable infectious disease since 2007, the comprehensive national health insurance data can still track its trend. Here, we used data from the open data portal of the Taiwan Centers for Disease Control. The scarlet fever trend was measured by outpatient and hospitalization rates from 2009 to 2017. In order to elucidate the spatio-temporal hotspots, we developed a new method named the spatio-temporal Gi* statistic, and applied Joinpoint regression to compute the annual percentage change (APC). The overall APCs in outpatient and hospitalization were 15.1% (95% CI: 10.3%-20.2%) and 7.7% (95%CI: 4.5% -10.9%). The major two infected groups were children aged 5-9 (outpatient: 0.138 scarlet fever diagnoses per 1,000 visits; inpatient: 2.579 per 1,000 visits) and aged 3-4 (outpatient: 0.084 per 1,000 visits; inpatient: 1.469 per 1,000 visits). We found the counties in eastern Taiwan and offshore counties had the most hotspots in the outpatient setting. In terms of hospitalization, the hotspots mostly occurred in offshore counties close to China. With the help of the spatio-temporal statistic, health workers can set up enhanced laboratory surveillance in those hotspots.

Identification of priority areas for surveillance of cutaneous leishmaniasis using spatial analysis approaches in Southeastern Brazil

BACKGROUND

Cutaneous leishmaniasis (CL) is an important public health problem in Brazil and in several tropical regions of the world. In the Americas, Brazil is the country with the highest number of registered cases. In Brazil, the state of Minas Gerais has the highest number of cases in the southeastern region. In the present study, we used spatial analysis in the State of Minas Gerais to identify municipalities of priority during a nine-year period (2007-2015), which might be used to guide surveillance and control measures.

METHODS

An ecological study with spatial analysis of autochthonous cases of CL was performed in the state of Minas Gerais between 2007 and 2015. We calculated incidence rates, used Empirical Bayesian smoothing for each municipality, and divided the analyses into three-year intervals. In order to analyze the existence of spatial autocorrelation, and to define priority areas, Moran's Global Index and Local Indicators of Spatial Association (LISA) were used.

RESULTS

The mean incidence rate for the entire state was 6.1/100,000 inhabitants. For Minas Gerais, analysis of CL cases over time revealed a successive increase of indicated mesoregions with high priority municipalities. Eight of the designated mesoregions contained municipalities classified as high priority areas in any of the three evaluated trienniums, and four mesoregions had high priority municipalities throughout the entire investigation.

CONCLUSIONS

Within the southeastern region of Brazil, Minas Gerais State stands out, with highest CL incidence rates. Using spatial analysis, we identified an increasing numbers of cases in the municipalities classified as high priority areas in different mesoregions of the state. This information might be of value to direct surveillance and control measures against CL and to understand the dynamics of the expansion of CL in Minas Gerais. Similar approaches might be used to map CL in other regions throughout Brazil, or in any other country, where national notification and control programs exist.

Influence of strategic points in the dispersion of Aedes aegypti in infested areas

OBJECTIVE

To evaluate whether sites with large amount of potential breeding sites for immature forms of Aedes aegypti, called strategic points, influence in the active vector's dispersion into properties in their surroundings.

METHODS

We selected four areas in the municipality of Campinas, three of them with strategic points classified as high, moderate, and low risk according to infestation and a control area, without strategic points. Between October 2015 and September 2016, we monthly installed oviposition traps and evaluated the infestation by Ae. aegypti in all properties of each selected area. To verify if there was vector dispersion from each strategic point, based on its location, we investigated the formation of clusters with excess of eggs or larvae or pupae containers, using the Gi spatial statistics.

RESULTS

The amount of eggs collected in the ovitraps and the number of positive containers for Ae. aegypti did not show clusters of high values concerning its distance from the strategic point. Both presented random distribution not spatially associated with the positioning of strategic points in the area.

CONCLUSIONS

Strategic points are not confirmed as responsible for the vector's dispersion for properties in their surroundings. We highlight the importance of reviewing the current strategy of the vector control program in Brazil, seeking a balance from the technical, operational, and economic point of view, without disregarding the role of strategic points as major producers of mosquitoes and their importance in the dissemination of arboviruses in periods of transmission.

Spatiotemporal Variation and Hot Spot Detection of Visceral Leishmaniasis Disease in Kashi Prefecture, China

Visceral leishmaniasis (VL) remains a serious public health problem in China. To explore the temporal, spatial, and spatiotemporal characteristics of visceral leishmaniasis (VL), the spatial and spatiotemporal clustering distribution and their relationships with the surrounding geographic environmental factors were analyzed. In this study, the average nearest-neighbor distance (ANN), Ripley's K-function and Moran's I statistics were used to evaluate spatial autocorrelation in the VL distribution of the existing case patterns. Getis⁻Ord Gi* was used to identify the hot-spot and cold-spot areas based on Geographic Information System (GIS), and spatiotemporal retrospective permutation scan statistics was used to detect the spatiotemporal clusters. The results indicated that VL continues to be a serious public health problem in Kashi Prefecture, China, particularly in the north-central region of Jiashi County, which is a relatively high-risk area in which hot spots are distributed. Autumn and winter months were the outbreak season for VL cases. The detection of spatial and spatiotemporal patterns can provide epidemiologists and local governments with significant information for prevention measures and control strategies.

Visceral leishmaniasis: Spatiotemporal heterogeneity and drivers underlying the hotspots in Muzaffarpur, Bihar, India

BACKGROUND

Despite the overall decrease in visceral leishmaniasis (VL) incidence on the Indian subcontinent, there remain spatiotemporal clusters or 'hotspots' of new cases. The characteristics of these hotspots, underlying transmission dynamics, and their importance for shaping control strategies are not yet fully understood and are investigated in this study for a VL endemic area of ~100,000 inhabitants in Bihar, India between 2007-2015.

METHODOLOGY/PRINCIPAL FINDINGS

VL incidence (cases/10,000/year) dropped from 12.3 in 2007 to 0.9 in 2015, which is just below the World Health Organizations' threshold for elimination as a public health problem. Clustering of VL was assessed between subvillages (hamlets), using multiple geospatial and (spatio)temporal autocorrelation and hotspot analyses. One to three hotspots were identified each year, often persisting for 1-5 successive years with a modal radius of ~500m. The relative risk of having VL was 5-86 times higher for inhabitants of hotspots, compared to those living outside hotspots. Hotspots harbour significantly more households from the two lowest asset quintiles (as proxy for socio-economic status). Overall, children and young adelescents (5-14 years) have the highest risk for VL, but within hotspots and at the start of outbreaks, older age groups (35+ years) show a comparable high risk.

CONCLUSIONS/SIGNIFICANCE

This study demonstrates significant spatiotemporal heterogeneity in VL incidence at subdistrict level. The association between poverty and hotspots confirms that VL is a disease of 'the poorest of the poor' and age patterns suggest a potential role of waning immunity as underlying driver of hotspots. The recommended insecticide spraying radius of 500m around detected VL cases corresponds to the modal hotspot radius found in this study. Additional data on immunity and asymptomatic infection, and the development of spatiotemporally explicit transmission models that simulate hotspot dynamics and predict the impact of interventions at the smaller geographical scale will be crucial tools in sustaining elimination.

Application of sandwich spatial estimation method in cancer mapping: A case study for breast cancer mortality in the Chinese mainland, 2005

High-accuracy spatial distribution estimation is crucial for cancer prevention and control. Due to their complicated pathogenic factors, the distributions of many cancers' mortalities appear blocky, and spatial heterogeneity is common. However, most of the commonly used cancer mapping methods are based on spatial autocorrelation theory. Sandwich estimation is a new method based on spatial heterogeneity theory. A modified sandwich estimation method suitable for the estimation of cancer mortality distribution is proposed in this study. The variances of cancer mortality data are used to fuse sandwich estimation results from various auxiliary variables, the feasibility of which in estimating cancer mortality distributions is explained theoretically. The breast cancer (BC) mortality of the Chinese mainland in 2005 was taken as a case, and the accuracy of the modified sandwich estimation method was compared with that of the Hierarchical Bayesian (HB), the Co-Kriging (CK) and the Ordinary Kriging (OK) methods. The accuracy of the modified sandwich estimation method was better than the HB, the CK and the OK methods, and the estimation result from the modified sandwich estimation method was more likely to be acceptable. Therefore, this study represents an attempt to apply the sandwich estimation method to the estimation of cancer mortality distributions with strong spatial heterogeneity, which holds great potential for further application.

Spatial Analysis of Rice Blast in China at Three Different Scales

In this study, spatial analyses were conducted at three different scales to better understand the epidemiology of rice blast, a major rice disease caused by Magnaporthe oryzae. At the regional scale, across the major rice production regions in China, rice blast incidence was monitored on 101 dates at 193 stations from 10 June to 10 September during 2009 to 2014, and surveyed in 143 fields in September 2016; at the county scale, three surveys were done covering one to five counties in 2015 to 2016; and, at the field scale, blast was evaluated in six fields in 2015 to 2016. Spatial cluster and hot spot analyses were conducted in the geographic information system on the geographical pattern of the disease at regional scale, and geostatistical analysis was performed at all three scales. Cluster and hot spot analyses revealed that high-disease areas were clustered in mountainous areas in China. Geostatistical analyses detected spatial dependence of blast incidence with influence ranges of 399 to 1,080 km at regional scale and 5 to 10 m at field scale but not at county scale. The spatial patterns at different scales might be determined by inherent properties of rice blast and environmental driving forces, and findings from this study provide helpful information to sampling and management of rice blast.

Integrated Social-Behavioral and Ecological Risk Maps to Prioritize Local Public Health Responses to Lyme Disease

BACKGROUND

The risk of contracting Lyme disease (LD) can vary spatially because of spatial heterogeneity in risk factors such as social-behavior and exposure to ecological risk factors. Integrating these risk factors to inform decision-making should therefore increase the effectiveness of mitigation interventions.

OBJECTIVES

The objective of this study was to develop an integrated social-behavioral and ecological risk-mapping approach to identify priority areas for LD interventions.

METHODS

The study was conducted in the Montérégie region of Southern Quebec, Canada, where LD is a newly endemic disease. Spatial variation in LD knowledge, risk perceptions, and behaviors in the population were measured using web survey data collected in 2012. These data were used as a proxy for the social-behavioral component of risk. Tick vector population densities were measured in the environment during field surveillance from 2007 to 2012 to provide an index of the ecological component of risk. Social-behavioral and ecological components of risk were combined with human population density to create integrated risk maps. Map predictions were validated by testing the association between high-risk areas and the current spatial distribution of human LD cases.

RESULTS

Social-behavioral and ecological components of LD risk had markedly different distributions within the study region, suggesting that both factors should be considered for locally adapted interventions. The occurrence of human LD cases in a municipality was positively associated with tick density (p<0.01) but was not significantly associated with social-behavioral risk.

CONCLUSION

This study is an applied demonstration of how integrated social-behavioral and ecological risk maps can be created to assist decision-making. Social survey data are a valuable but underutilized source of information for understanding regional variation in LD exposure, and integrating this information into risk maps provides a novel approach for prioritizing and adapting interventions to the local characteristics of target populations. https://doi.org/10.1289/EHP1943.

Trends in spatio-temporal dynamics of visceral leishmaniasis cases in a highly-endemic focus of Bihar, India: an investigation based on GIS tools

Background

Visceral leishmaniasis (VL) in Bihar State (India) continues to be endemic, despite the existence of effective treatment and a vector control program to control disease morbidity. A clear understanding of spatio-temporal distribution of VL may improve surveillance and control implementation. This study explored the trends in spatio-temporal dynamics of VL endemicity at a meso-scale level in Vaishali District, based on geographical information systems (GIS) tools and spatial statistical analysis.

Methods

A GIS database was used to integrate the VL case data from the study area between 2009 and 2014. All cases were spatially linked at a meso-scale level. Geospatial techniques, such as GIS-layer overlaying and mapping, were employed to visualize and detect the spatio-temporal patterns of a VL endemic outbreak across the district. The spatial statistic Moran’s I Index (Moran’s I) was used to simultaneously evaluate spatial-correlation between endemic villages and the spatial distribution patterns based on both the village location and the case incidence rate (CIR). Descriptive statistics such as mean, standard error, confidence intervals and percentages were used to summarize the VL case data.

Results

There were 624 endemic villages with 2719 (average 906 cases/year) VL cases during 2012–2014. The Moran’s I revealed a cluster pattern (P < 0.05) of CIR distribution at the meso-scale level. On average, 68 villages were newly-endemic each year. Of which 93.1% of villages’ endemicity were found to have occurred on the peripheries of the previous year endemic villages. The mean CIR of the endemic villages that were peripheral to the following year newly-endemic villages, compared to all endemic villages of the same year, was higher (P < 0.05).

Conclusion

The results show that the VL endemicity of new villages tends to occur on the periphery of villages endemic in the previous year. High-CIR plays a major role in the spatial dispersion of the VL cases between non-endemic and endemic villages. This information can help achieve VL elimination throughout the Indian subcontinent by improving vector control design and implementation in highly-endemic district.

Spatial distribution and cluster analysis of dengue using self organizing maps in Andhra Pradesh, India, 2011-2013

BACKGROUND AND OBJECTIVES

Dengue is an emerging and re-emerging infectious disease, transmitted by mosquitoes. It is mostly prevalent in tropical and sub-tropical regions of the world, particularly, in Asia-Pacific region. To understand the epidemiology and spatial distribution of dengue, a retrospective surveillance study was conducted in the state of Andhra Pradesh, India during 2011-2013.

MATERIAL AND METHODS

District-wise disease endemicity levels were mapped through geographical information system (GIS) tools. Spatial statistical analysis such as Getis-Ord Gi* was performed to identify hot spots and cold spots of dengue disease. Similarly self organizing maps (SOM), a datamining tool was also applied to understand the endemicity patterns in study areas.

RESULTS

The analysis shows that districts of Warangal, Karimnagar, Khammam and Vizianagaram are reported as hot spot regions whereas Adilabad and Nizamabad reported as cold spots for dengue. The SOM classify 23 districts in 03 major (07 sub) clusters. These SOM clusters were projected in the geographical space and based on the disease/cases intensity the districts were characterized into low, medium and high endemic areas.

CONCLUSION

This visualization approach, SOM-GIS helps the public health officials to identify the disease endemic zones and take real time decisions for disease management.

Variations in visceral leishmaniasis burden, mortality and the pathway to care within Bihar, India

BACKGROUND

Visceral leishmaniasis (VL) has been targeted by the WHO for elimination as a public health problem (< 1 case/10,000 people/year) in the Indian sub-continent (ISC) by 2020. Bihar State in India, which accounts for the majority of cases in the ISC, remains a major target for this elimination effort. However, there is considerable spatial, temporal and sub-population variation in occurrence of the disease and the pathway to care, which is largely unexplored and a threat to achieving the target.

METHODS

Data from 6081 suspected VL patients who reported being clinically diagnosed during 2012-2013 across eight districts in Bihar were analysed. Graphical comparisons and Chi-square tests were used to determine differences in the burden of identified cases by season, district, age and sex. Log-linear regression models were fitted to onset (of symptoms)-to-diagnosis and onset-to-treatment waiting times to estimate their associations with age, sex, district and various socio-economic factors (SEFs). Logistic regression models were used to identify factors associated with mortality.

RESULTS

Comparisons of VL caseloads suggested an annual cycle peaking in January-March. A 17-fold variation in the burden of identified cases across districts and under-representation of young children (0-5 years) relative to age-specific populations in Bihar were observed. Women accounted for a significantly lower proportion of the reported cases than men (41 vs 59%, P < 0.0001). Age, district of residence, house wall materials, caste, treatment cost, travelling for diagnosis and the number of treatments for symptoms before diagnosis were identified as correlates of waiting times. Mortality was associated with age, district of residence, onset-to-treatment waiting time, treatment duration, cattle ownership and cost of diagnosis.

CONCLUSIONS

The distribution of VL in Bihar is highly heterogeneous, and reported caseloads and associated mortality vary significantly across different districts, posing different challenges to the elimination campaign. Socio-economic factors are important correlates of these differences, suggesting that elimination will require tailoring to population and sub-population circumstances.

Combining epidemiology with basic biology of sand flies, parasites, and hosts to inform leishmaniasis transmission dynamics and control

Quantitation of the nonlinear heterogeneities in Leishmania parasites, sand fly vectors, and mammalian host relationships provides insights to better understand leishmanial transmission epidemiology towards improving its control. The parasite manipulates the sand fly via production of promastigote secretory gel (PSG), leading to the “blocked sand fly” phenotype, persistent feeding attempts, and feeding on multiple hosts. PSG is injected into the mammalian host with the parasite and promotes the establishment of infection. Animal models demonstrate that sand flies with the highest parasite loads and percent metacyclic promastigotes transmit more parasites with greater frequency, resulting in higher load infections that are more likely to be both symptomatic and efficient reservoirs. The existence of mammalian and sand fly “super-spreaders” provides a biological basis for the spatial and temporal clustering of clinical leishmanial disease. Sand fly blood-feeding behavior will determine the efficacies of indoor residual spraying, topical insecticides, and bed nets. Interventions need to have sufficient coverage to include transmission hot spots, especially in the absence of field tools to assess infectiousness. Interventions that reduce sand fly densities in the absence of elimination could have negative consequences, for example, by interfering with partial immunity conferred by exposure to sand fly saliva. A deeper understanding of both sand fly and host biology and behavior is essential to ensuring effectiveness of vector interventions.

We review recent research that sheds light on the quantitative biology of leishmanial transmission between sand flies and mammalian hosts and use these insights to better understand transmission, the observed epidemiology of the disease, and their implications in choice of control strategy. Using animal models, we show how the parasite-induced processes manipulate sand fly blood-feeding behavior and the infectious metacyclic dose to promote host infection and to differentially regulate the onward transmission potential of individual vectors and hosts. The existence of subpopulations of mammalian and sand fly “super-spreaders” provides a biological basis for the spatial and temporal clustering of clinical leishmanial disease. While tools are unavailable to distinguish these individuals in mixed populations, blanket interventions will be necessary to ensure inclusion of transmission hot spots. Interventions that reduce sand fly densities without elimination could interfere with vector—host dynamics and conferred partial immunity to host populations.

Spatial and temporal trends of visceral leishmaniasis by mesoregion in a southeastern state of Brazil, 2002-2013

BACKGROUND

Visceral leishmaniasis (VL) is expanding in Brazil and in other South American countries, a process that has been associated with the urbanization of the disease. This study analyzes the spatial and temporal distribution of VL in the Brazilian state of Minas Gerais and identifies the areas with higher risks of transmission.

METHODOLOGY

An ecological study with spatial and time series analyzes of new confirmed cases of VL notified to the Brazilian Notifiable Disease Information System between 2002 and 2013, considering the 12 mesoregions of Minas Gerais. Two complementary methodologies were used: thematic maps of incidence and Poisson (log-linear) generalized linear model. Thematic maps using crude and smoothed cumulative incidences were generated for four trienniums. Poisson Regression measured the variation of the average number of cases from one year to the following, for each mesoregion.

PRINCIPAL FINDINGS

The 5,778 cases analyzed revealed a heterogeneous spatial and temporal distribution of VL in Minas Gerais. Six mesoregions (Central Mineira, Jequitinhonha, Metropolitan area of Belo Horizonte, Northwest of Minas, North of Minas, and Vale do Rio Doce) were responsible for the expansion and maintenance of VL, with incidence rates as high as 26/100,000 inhabitants. The Vale do Rio Doce and Jequitinhonha mesoregions showed a considerable increase in the incidence rates in the last period studied. The other six mesoregions reported only sporadic cases and presented low and unsteady incidence rates, reaching a maximum of 1.2/100,000 inhabitants.

CONCLUSIONS/SIGNIFICANCE

The results contribute to further the current understanding about the expansion of VL in Minas Gerais and may help guide actions for disease control.

Comparative analyses of whole genome sequences of Leishmania infantum isolates from humans and dogs in northeastern Brazil

The genomic sequences of 20 Leishmania infantum isolates collected in northeastern Brazil were compared with each other and with the available genomic sequences of 29 L. infantum/donovani isolates from Nepal and Turkey. The Brazilian isolates were obtained in the early 1990s or since 2009 from patients with visceral or non-ulcerating cutaneous leishmaniasis, asymptomatic humans, or dogs with visceral leishmaniasis. Two isolates were from the blood and bone marrow of the same visceral leishmaniasis patient. All 20 genomic sequences display 99.95% identity with each other and slightly less identity with a reference L. infantum genome from a Spanish isolate. Despite the high identity, analysis of individual differences among the 32 million base pair genomes showed sufficient variation to allow the isolates to be clustered based on the primary sequence. A major source of variation detected was in chromosome somy, with only four of the 36 chromosomes being predominantly disomic in all 49 isolates examined. In contrast, chromosome 31 was predominantly tetrasomic/pentasomic, consistent with its regions of synteny on two different disomic chromosomes of Trypanosoma brucei. In the Brazilian isolates, evidence for recombination was detected in 27 of the 36 chromosomes. Clustering analyses suggested two populations, in which two of the five older isolates from the 1990s clustered with a majority of recent isolates. Overall the analyses do not suggest individual sequence variants account for differences in clinical outcome or adaptation to different hosts. For the first known time, DNA of isolates from asymptomatic subjects were sequenced. Of interest, these displayed lower diversity than isolates from symptomatic subjects, an observation that deserves further investigation with additional isolates from asymptomatic subjects.

Knockdown resistance mutations predict DDT resistance and pyrethroid tolerance in the visceral leishmaniasis vector Phlebotomus argentipes

BACKGROUND

Indoor residual spraying (IRS) with DDT has been the primary strategy for control of the visceral leishmaniasis (VL) vector Phlebotomus argentipes in India but efficacy may be compromised by resistance. Synthetic pyrethroids are now being introduced for IRS, but with a shared target site, the para voltage-gated sodium channel (VGSC), mutations affecting both insecticide classes could provide cross-resistance and represent a threat to sustainable IRS-based disease control.

METHODOLOGY/PRINCIPAL FINDINGS

A region of the Vgsc gene was sequenced in P. argentipes from the VL hotspot of Bihar, India. Two knockdown resistance (kdr) mutations were detected at codon 1014 (L1014F and L1014S), each common in mosquitoes, but previously unknown in phlebotomines. Both kdr mutations appear largely recessive, but as homozygotes (especially 1014F/F) or as 1014F/S heterozygotes exert a strong effect on DDT resistance, and significantly predict survivorship to class II pyrethroids in short-duration bioassays. The mutations are present at high frequency in wild P. argentipes populations from Bihar, with 1014F significantly more common in higher VL areas.

CONCLUSIONS/SIGNIFICANCE

The Vgsc mutations detected appear to be a primary mechanism underlying DDT resistance in P. argentipes and a contributory factor in reduced pyrethroid susceptibility, suggesting a potential impact if P. argentipes are subjected to suboptimal levels of pyrethroid exposure, or additional resistance mechanisms evolve. The assays to detect kdr frequency changes provide a sensitive, high-throughput monitoring tool to detecting spatial and temporal variation in resistance in P. argentipes.

Elimination of visceral leishmaniasis in the Indian subcontinent: a comparison of predictions from three transmission models

We present three transmission models of visceral leishmaniasis (VL) in the Indian subcontinent (ISC) with structural differences regarding the disease stage that provides the main contribution to transmission, including models with a prominent role of asymptomatic infection, and fit them to recent case data from 8 endemic districts in Bihar, India. Following a geographical cross-validation of the models, we compare their predictions for achieving the WHO VL elimination targets with ongoing treatment and vector control strategies. All the transmission models suggest that the WHO elimination target (<1 new VL case per 10,000 capita per year at sub-district level) is likely to be met in Bihar, India, before or close to 2020 in sub-districts with a pre-control incidence of 10 VL cases per 10,000 people per year or less, when current intervention levels (60% coverage of indoor residual spraying (IRS) of insecticide and a delay of 40days from onset of symptoms to treatment (OT)) are maintained, given the accuracy and generalizability of the existing data regarding incidence and IRS coverage. In settings with a pre-control endemicity level of 5/10,000, increasing the effective IRS coverage from 60 to 80% is predicted to lead to elimination of VL 1-3 years earlier (depending on the particular model), and decreasing OT from 40 to 20days to bring elimination forward by approximately 1year. However, in all instances the models suggest that L. donovani transmission will continue after 2020 and thus that surveillance and control measures need to remain in place until the longer-term aim of breaking transmission is achieved.

Evaluating drug resistance in visceral leishmaniasis: the challenges

For decades antimonials were the drugs of choice for the treatment of visceral leishmaniasis (VL), but the recent emergence of resistance has made them redundant as first-line therapy in the endemic VL region in the Indian subcontinent. The application of other drugs has been limited due to adverse effects, perceived high cost, need for parenteral administration and increasing rate of treatment failures. Liposomal amphotericin B (AmB) and miltefosine (MIL) have been positioned as the effective first-line treatments; however, the number of monotherapy MIL-failures has increased after a decade of use. Since no validated molecular resistance markers are yet available, monitoring and surveillance of changes in drug sensitivity and resistance still depends on standard phenotypic in vitro promastigote or amastigote susceptibility assays. Clinical isolates displaying defined MIL- or AmB-resistance are still fairly scarce and fundamental and applied research on resistance mechanisms and dynamics remains largely dependent on laboratory-generated drug resistant strains. This review addresses the various challenges associated with drug susceptibility and -resistance monitoring in VL, with particular emphasis on the choice of strains, susceptibility model selection and standardization of procedures with specific read-out parameters and well-defined threshold criteria. The latter are essential to support surveillance systems and safeguard the limited number of currently available antileishmanial drugs.

Cutaneous leishmaniasis prevalence and morbidity based on environmental factors in Ilam, Iran: Spatial analysis and land use regression models

The aim of this study was to investigate the impact of the environmental factors on cutaneous leishmaniasis (CL) prevalence and morbidity in Ilam province, western Iran, as a known endemic area for this disease. Accurate locations of 3237 CL patients diagnosed from 2013 to 2015, their demographic information, and data of 17 potentially predictive environmental variables (PPEVs) were prepared to be used in Geographic Information System (GIS) and Land-Use Regression (LUR) analysis. The prevalence, risk, and predictive risk maps were provided using Inverse Distance Weighting (IDW) model in GIS software. Regression analysis was used to determine how environmental variables affect on CL prevalence. All maps and regression models were developed based on the annual and three-year average of the CL prevalence. The results showed that there was statistically significant relationship (P value≤0.05) between CL prevalence and 11 (64%) PPEVs which were elevation, population, rainfall, temperature, urban land use, poorland, dry farming, inceptisol and aridisol soils, and forest and irrigated lands. The highest probability of the CL prevalence was predicted in the west of the study area and frontier with Iraq. An inverse relationship was found between CL prevalence and environmental factors, including elevation, covering soil, rainfall, agricultural irrigation, and elevation while this relation was positive for temperature, urban land use, and population density. Environmental factors were found to be an important predictive variables for CL prevalence and should be considered in management strategies for CL control.

Risk Mapping and Situational Analysis of Cutaneous Leishmaniasis in an Endemic Area of Central Iran: A GIS-Based Survey

INTRODUCTION

Cutaneous leishmaniasis (CL) is among the top 10 infectious disease priorities in the world, and the leading cause of morbidity in Iran. The present study was conducted to assess the risk of CL, and to determine some epidemiological features of the disease in endemic areas of Qom Province in Central Iran during 2009 to 2013.

METHODS

Data regarding human cases of the disease were obtained from the Qom Province Health Center, prepared and stored in a spatial database created in ArcGIS10.3. A total of 9 out of 212 Leishmania spp. positive slides taken in 2013 from patients residing in Qom city were examined using molecular methods and the species of Leishmania was identified by PCR-RFLP. Those 9 patients had no history of travel outside the city. Spatial analysis and clustering methods were applied to find major hot spots and susceptible areas for the establishment of novel foci of the disease. Transmission patterns were examined for spatial autocorrelation using the Moran's I statistical application, and for the clustering of high or low values using the Getis-Ord Gi* statistics.

RESULTS

During the period of study, a total of 1767 CL cases were passively reported in the area, out of which were 65% males and 35% females. The highest and lowest numbers of cases were reported in 2010 and 2013, respectively. Importantly, 979 cases were reported from urban areas, while the remainder came from rural areas. Leishmania major was detected as the causative agent of CL in the city of Qom. Remarkably, most patients recorded in Qom city were associated with a history of travel to the endemic areas of CL within the province, or to other endemic areas of the disease in Iran. Spatial distribution of CL cases revealed northeastern and southwestern quarters of the city were the major hot spots of the disease (P<0.05). Hot spot and CL transmission risk analysis across the province indicated that more than 40 villages were located in high and very high risk areas of CL transmission.

CONCLUSIONS

Due to the existence of hot spots (P<0.05) of CL in successive years in some quarters of Qom city, along with detection of L. major from the patients without a history of travel, there may be potential of local transmission of the disease within the city. Therefore, it is necessary to conduct a comprehensive study concerning the hot spots of CL in Qom city for curtailing the incidence of the disease in the city. The methodology and the results of this study is essential in serving as a yardstick for subsequent similar studies that will be carried out in other endemic areas of CL in Iran and providing an adequate tool for the establishment of a national database of cutaneous leishmaniasis.

Detection of differentially methylated regions in whole genome bisulfite sequencing data using local Getis-Ord statistics

MOTIVATION

DNA methylation is an important epigenetic modification that has essential role in gene regulation, cell differentiation and cancer development. Bisulfite sequencing is a widely used technique to obtain genome-wide DNA methylation profiles, and one of the key tasks of analyzing bisulfite sequencing data is to detect differentially methylated regions (DMRs) among samples under different treatment conditions. Although numerous tools have been proposed to detect differentially methylated single CpG site (DMC) between samples, methods for direct DMR detection, especially for complex study designs, are largely limited.

RESULTS

We present a new software, GetisDMR, for direct DMR detection. We use beta-binomial regression to model the whole-genome bisulfite sequencing data, where variations in methylation levels and confounding effects have been accounted for. We employ a region-wise test statistic, which is derived from local Getis-Ord statistics and considers the spatial correlation between nearby CpG sites, to detect DMRs. Unlike existing methods, that attempt to infer DMRs from DMCs based on empirical criteria, we provide statistical inference for direct DMR detection. Through extensive simulations and an application to two mouse datasets, we demonstrate that GetisDMR achieves better sensitivities, positive predictive values, more exact locations and better agreement of DMRs with current biological knowledge.

AVAILABILITY AND IMPLEMENTATION

It is available at https://github.com/DMU-lilab/GetisDMR CONTACTS: y.wen@auckland.ac.nz or zhiguangli@dlmedu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.

A Novel Molecular Test to Diagnose Canine Visceral Leishmaniasis at the Point of Care

Dogs are the principal reservoir hosts of zoonotic visceral leishmaniasis (VL) but current serological methods are not sensitive enough to detect all subclinically infected animals, which is crucial to VL control programs. Polymerase chain reaction (PCR) methods have greater sensitivity but require expensive equipment and trained personnel, impairing its implementation in endemic areas. We developed a diagnostic test that uses isothermal recombinase polymerase amplification (RPA) to detect Leishmania infantum. This method was coupled with lateral flow (LF) reading with the naked eye to be adapted as a point-of-care test. The L. infantum RPA-LF had an analytical sensitivity similar to real time-PCR, detecting DNA of 0.1 parasites spiked in dog blood, which was equivalent to 40 parasites/mL. There was no cross amplification with dog or human DNA or with Leishmania braziliensis, Leishmania amazonensis, or Trypanosoma cruzi. The test also amplified Leishmania donovani strains (N = 7). In a group of clinically normal dogs (N = 30), RPA-LF detected more subclinical infections than rK39 strip test, a standard serological method (50% versus 13.3% positivity, respectively; P = 0.005). Also, RPA-LF detected L. infantum in noninvasive mucosal samples of dogs with a sensitivity comparable to blood samples. This novel molecular test may have a positive impact in leishmaniasis control programs.

Insilico analysis of hypothetical proteins unveils putative metabolic pathways and essential genes in Leishmania donovani

Leishmaniasis is a parasitic disease caused by the protozoan Leishmania, which is active in two broad forms namely, Visceral Leishmaniasis (VL or Kala Azar) and Cutaneous Leishmaniasis (CL). The disease is most prevalent in the tropical regions and poses a threat to over 70 countries across the globe. About 200 million people are estimated to be at risk of developing VL in the Indian subcontinent, and this refers to around 67% of the global VL disease burden. The Indian state of Bihar alone accounts for 50% of the total VL cases. While no vaccination exists, several pentavalent antimonials and drugs like Paromomycin, Amphotericin, Miltefosine etc. are used in the treatment of Leishmaniasis. However, due to their low efficacies and the resistance developed by the bug to these medications, there is an urgent need to look into newer species specific targets. The proteome information available suggests that among the 7960 proteins in Leishmania donavani, a staggering 65% remains classified as a hypothetical uncharacterized set. In this background, we have attempted to assign probable functions to these hypothetical sequences present in this parasite, to explore their plausible roles as druggable receptors. Thus, putative functions have been defined to 105 hypothetical proteins, which exhibited a GO term correlation and PFAM domain coverage of more than 50% over the query sequence length. Of these, 27 sequences were found to be associated with a reference pathway in KEGG as well. Further, using homology approaches, four pathways viz., Ubiquinone biosynthesis, Fatty acid elongation in Mitochondria, Fatty Acid Elongation in ER and Seleno-cysteine Metabolism have been reconstructed. In addition, 7 new putative essential genes have been mined with the help of Eukaryotic Database of Essential Genes (DEG). All these information related to pathways and essential genes indeed show promise for exploiting the select molecules as potential therapeutic targets.

Mortality and case fatality due to visceral leishmaniasis in Brazil: a nationwide analysis of epidemiology, trends and spatial patterns

BACKGROUND

Visceral leishmaniasis (VL) is a significant public health problem in Brazil and several regions of the world. This study investigated the magnitude, temporal trends and spatial distribution of mortality related to VL in Brazil.

METHODS

We performed a study based on secondary data obtained from the Brazilian Mortality Information System. We included all deaths in Brazil from 2000 to 2011, in which VL was recorded as cause of death. We present epidemiological characteristics, trend analysis of mortality and case fatality rates by joinpoint regression models, and spatial analysis using municipalities as geographical units of analysis.

RESULTS

In the study period, 12,491,280 deaths were recorded in Brazil. VL was mentioned in 3,322 (0.03%) deaths. Average annual age-adjusted mortality rate was 0.15 deaths per 100,000 inhabitants and case fatality rate 8.1%. Highest mortality rates were observed in males (0.19 deaths/100,000 inhabitants), <1 year-olds (1.03 deaths/100,000 inhabitants) and residents in Northeast region (0.30 deaths/100,000 inhabitants). Highest case fatality rates were observed in males (8.8%), ≥ 70 year-olds (43.8%) and residents in South region (17.7%). Mortality and case fatality rates showed a significant increase in Brazil over the period, with different patterns between regions: increasing mortality rates in the North (Annual Percent Change--APC: 9.4%; 95% confidence interval--CI: 5.3 to 13.6), and Southeast (APC: 8.1%; 95% CI: 2.6 to 13.9); and increasing case fatality rates in the Northeast (APC: 4.0%; 95% CI: 0.8 to 7.4). Spatial analysis identified a major cluster of high mortality encompassing a wide geographic range in North and Northeast Brazil.

CONCLUSIONS

Despite ongoing control strategies, mortality related to VL in Brazil is increasing. Mortality and case fatality vary considerably between regions, and surveillance and control measures should be prioritized in high-risk clusters. Early diagnosis and treatment are fundamental strategies for reducing case fatality of VL in Brazil.