Biosensors based on peptide exposure show single molecule conformations in live cells.

We describe an approach to study the conformation of individual proteins during single particle tracking (SPT) in living cells. "Binder/tag" is based on incorporation of a 7-mer peptide (the tag) into a protein where its solvent exposure is controlled by protein conformation. Only upon exposure can the peptide specifically interact with a reporter protein (the binder). Thus, simple fluorescence localization reflects protein conformation. Through direct excitation of bright dyes, the trajectory and conformation of individual proteins can be followed. Simple protein engineering provides highly specific biosensors suitable for SPT and FRET. We describe tagSrc, tagFyn, tagSyk, tagFAK, and an orthogonal binder/tag pair. SPT showed slowly diffusing islands of activated Src within Src clusters and dynamics of activation in adhesions. Quantitative analysis and stochastic modeling revealed in vivo Src kinetics. The simplicity of binder/tag can provide access to diverse proteins.

Two-step spatiotemporal anomaly detection corrected for lag reporting time with application to real-time dengue surveillance in Thailand.

BACKGROUND: Dengue infection ranges from asymptomatic to severe and life-threatening, with no specific treatment available. Vector control is crucial for interrupting its transmission cycle. Accurate estimation of outbreak timing and location is essential for efficient resource allocation. Timely and reliable notification systems are necessary to monitor dengue incidence, including spatial and temporal distributions, to detect outbreaks promptly and implement effective control measures. METHODS: We proposed an integrated two-step methodology for real-time spatiotemporal cluster detection, accounting for reporting delays. In the first step, we employed space-time nowcasting modeling to compensate for lags in the reporting system. Subsequently, anomaly detection methods were applied to assess adverse risks. To illustrate the effectiveness of these detection methods, we conducted a case study using weekly dengue surveillance data from Thailand. RESULTS: The developed methodology demonstrated robust surveillance effectiveness. By combining space-time nowcasting modeling and anomaly detection, we achieved enhanced detection capabilities, accounting for reporting delays and identifying clusters of elevated risk in real-time. The case study in Thailand showcased the practical application of our methodology, enabling timely initiation of disease control activities. CONCLUSION: Our integrated two-step methodology provides a valuable approach for real-time spatiotemporal cluster detection in dengue surveillance. By addressing reporting delays and incorporating anomaly detection, it complements existing surveillance systems and forecasting efforts. Implementing this methodology can facilitate the timely initiation of disease control activities, contributing to more effective prevention and control strategies for dengue in Thailand and potentially other regions facing similar challenges.

Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand.

BACKGROUND: Dengue is a mosquito-borne disease that causes over 300 million infections worldwide each year with no specific treatment available. Effective surveillance systems are needed for outbreak detection and resource allocation. Spatial cluster detection methods are commonly used, but no general guidance exists on the most appropriate method for dengue surveillance. Therefore, a comprehensive study is needed to assess different methods and provide guidance for dengue surveillance programs. METHODS: To evaluate the effectiveness of different cluster detection methods for dengue surveillance, we selected and assessed commonly used methods: Getis Ord [Formula: see text], Local Moran, SaTScan, and Bayesian modeling. We conducted a simulation study to compare their performance in detecting clusters, and applied all methods to a case study of dengue surveillance in Thailand in 2019 to further evaluate their practical utility. RESULTS: In the simulation study, Getis Ord [Formula: see text] and Local Moran had similar performance, with most misdetections occurring at cluster boundaries and isolated hotspots. SaTScan showed better precision but was less effective at detecting inner outliers, although it performed well on large outbreaks. Bayesian convolution modeling had the highest overall precision in the simulation study. In the dengue case study in Thailand, Getis Ord [Formula: see text] and Local Moran missed most disease clusters, while SaTScan was mostly able to detect a large cluster. Bayesian disease mapping seemed to be the most effective, with adaptive detection of irregularly shaped disease anomalies. CONCLUSIONS: Bayesian modeling showed to be the most effective method, demonstrating the best accuracy in adaptively identifying irregularly shaped disease anomalies. In contrast, SaTScan excelled in detecting large outbreaks and regular forms. This study provides empirical evidence for the selection of appropriate tools for dengue surveillance in Thailand, with potential applicability to other disease control programs in similar settings.

Advancing Dialogue About Consent and Molecular HIV Surveillance in the United States: Four Proposals Following a Federal Advisory Panel's Call for Major Reforms.

Policy Points Molecular HIV surveillance and cluster detection and response (MHS/CDR) programs have been a core public health activity in the United States since 2018 and are the "fourth pillar" of the Ending the HIV Epidemic initiative launched in 2019. MHS/CDR has caused controversy, including calls for a moratorium from networks of people living with HIV. In October 2022, the Presidential Advisory Council on HIV/AIDS (PACHA) adopted a resolution calling for major reforms. We analyze the policy landscape and present four proposals to federal stakeholders pertaining to PACHA's recommendations about incorporating opt-outs and plain-language notifications into MHS/CDR programs.

Optimizing the maximum reported cluster size for the multinomial-based spatial scan statistic.

BACKGROUND: Correctly identifying spatial disease cluster is a fundamental concern in public health and epidemiology. The spatial scan statistic is widely used for detecting spatial disease clusters in spatial epidemiology and disease surveillance. Many studies default to a maximum reported cluster size (MRCS) set at 50% of the total population when searching for spatial clusters. However, this default setting can sometimes report clusters larger than true clusters, which include less relevant regions. For the Poisson, Bernoulli, ordinal, normal, and exponential models, a Gini coefficient has been developed to optimize the MRCS. Yet, no measure is available for the multinomial model. RESULTS: We propose two versions of a spatial cluster information criterion (SCIC) for selecting the optimal MRCS value for the multinomial-based spatial scan statistic. Our simulation study suggests that SCIC improves the accuracy of reporting true clusters. Analysis of the Korea Community Health Survey (KCHS) data further demonstrates that our method identifies more meaningful small clusters compared to the default setting. CONCLUSIONS: Our method focuses on improving the performance of the spatial scan statistic by optimizing the MRCS value when using the multinomial model. In public health and disease surveillance, the proposed method can be used to provide more accurate and meaningful spatial cluster detection for multinomial data, such as disease subtypes.

HIV Response Interventions that Integrate HIV Molecular Cluster and Social Network Analysis: A Systematic Review.

Due to improved efficiency and reduced cost of viral sequencing, molecular cluster analysis can be feasibly utilized alongside existing human immunodeficiency virus (HIV) prevention strategies. The goal of this paper is to elucidate how HIV molecular cluster and social network analyses are being integrated to implement HIV response interventions. We searched PubMed, Scopus, PsycINFO, and Cochrane Library databases for studies incorporating both HIV molecular cluster and social network data. We identified 32 articles that combined analyses of HIV molecular sequences and social or sexual networks. All studies were descriptive. Six studies described network interventions informed by molecular and social data but did not fully evaluate their efficacy. There is no current standard for incorporating molecular and social network analyses to inform interventions or data demonstrating its utility. More research must be conducted to delineate benefits and best practices for leveraging molecular data for network-based interventions.

RESUMEN: Debido a mejor eficiencia y costo reducido de la secuenciación viral, el análisis de complejos moleculares se puede utilizar de manera factible junto con las estrategias de prevención del virus de inmunodeficiencia humana (VIH) existentes. El objetivo de este repaso es de aclarar como integrar los análisis de las redes sociales y de los complejos moleculares del VIH para implementar intervenciones para controlar el VIH. Buscamos en las bases de datos de PubMed, Scopus, PsycINFO y Cochrane Library por estudios que incorporaran datos de redes sociales y grupos moleculares del VIH. Identificamos 32 estudios que combinaban análisis de secuencias moleculares del VIH y datos de redes sociales. Todos los estudios fueron descriptivos. Seis estudios describieron intervenciones informadas por datos moleculares y sociales, pero no evaluaron completamente su eficacia. No existe un estándar actual para incorporar análisis moleculares y sociales para informar intervenciones o datos que demuestren su eficacia. Se deben realizar más investigaciones para delinear los beneficios y las mejores prácticas de aplicar los datos moleculares y sociales para crear intervenciones del VIH.

A simulation study for geographic cluster detection analysis on population-based health survey data using spatial scan statistics.

BACKGROUND: In public health and epidemiology, spatial scan statistics can be used to identify spatial cluster patterns of health-related outcomes from population-based health survey data. Although it is appropriate to consider the complex sample design and sampling weight when analyzing complex sample survey data, the observed survey responses without these considerations are often used in many studies related to spatial cluster detection. METHODS: We conducted a simulation study to investigate which data type from complex survey data is more suitable for use by comparing the spatial cluster detection results of three approaches: (1) individual-level data, (2) weighted individual-level data, and (3) aggregated data. RESULTS: The results of the spatial cluster detection varied depending on the data type. To compare the performance of spatial cluster detection, sensitivity and positive predictive value (PPV) were evaluated over 100 iterations. The average sensitivity was high for all three approaches, but the average PPV was higher when using aggregated data than when using individual-level data with or without sampling weights. CONCLUSIONS: Through the simulation study, we found that use of aggregate-level data is more appropriate than other types of data, when searching for spatial clusters using spatial scan statistics on population-based health survey data.

Clustered spatio-temporal varying coefficient regression model.

In regression analysis for spatio-temporal data, identifying clusters of spatial units over time in a regression coefficient could provide insight into the unique relationship between a response and covariates in certain subdomains of space and time windows relative to the background in other parts of the spatial domain and the time period of interest. In this article, we propose a varying coefficient regression method for spatial data repeatedly sampled over time, with heterogeneity in regression coefficients across both space and over time. In particular, we extend a varying coefficient regression model for spatial-only data to spatio-temporal data with flexible temporal patterns. We consider the detection of a potential cylindrical cluster of regression coefficients based on testing whether the regression coefficient is the same or not over the entire spatial domain for each time point. For multiple clusters, we develop a sequential identification approach. We assess the power and identification of known clusters via a simulation study. Our proposed methodology is illustrated by the analysis of a cancer mortality dataset in the Southeast of the U.S.

Optimizing the maximum reported cluster size in the spatial scan statistic for survival data.

BACKGROUND: The spatial scan statistic is a useful tool for cluster detection analysis in geographical disease surveillance. The method requires users to specify the maximum scanning window size or the maximum reported cluster size (MRCS), which is often set to 50% of the total population. It is important to optimize the maximum reported cluster size, keeping the maximum scanning window size at as large as 50% of the total population, to obtain valid and meaningful results. RESULTS: We developed a measure, a Gini coefficient, to optimize the maximum reported cluster size for the exponential-based spatial scan statistic. The simulation study showed that the proposed method mostly selected the optimal MRCS, similar to the true cluster size. The detection accuracy was higher for the best chosen MRCS than at the default setting. The application of the method to the Korea Community Health Survey data supported that the proposed method can optimize the MRCS in spatial cluster detection analysis for survival data. CONCLUSIONS: Using the Gini coefficient in the exponential-based spatial scan statistic can be very helpful for reporting more refined and informative clusters for survival data.

A functional-model-adjusted spatial scan statistic.

This paper introduces a new spatial scan statistic designed to adjust cluster detection for longitudinal confounding factors indexed in space. The functional-model-adjusted statistic was developed using generalized functional linear models in which longitudinal confounding factors were considered to be functional covariates. A general framework was developed for application to various probability models. Application to a Poisson model showed that the new method is equivalent to a conventional spatial scan statistic that adjusts the underlying population for covariates. In a simulation study with single and multiple covariate models, we found that our new method adjusts the cluster detection procedure more accurately than other methods. Use of the new spatial scan statistic was illustrated by analyzing data on premature mortality in France over the period from 1998 to 2013, with the quarterly unemployment rate as a longitudinal confounding factor.

Spatial clustering of suicide mortality and associated community characteristics in Kanagawa prefecture, Japan, 2011-2017.

BACKGROUND: Suicide mortality is high in Japan and early interventional strategies to solve that problem are needed. An accurate evaluation of the regional status of current suicide mortality would be useful for community interventions. A few studies in Kanagawa prefecture, located next to Tokyo and with the second largest population in Japan, have identified spatial clusters of suicide mortality at regional levels. This study examined spatial clustering and clustering over time of such events using spatial data from regional statistics on suicide deaths. METHODS: Data were obtained from regional statistics (58 regions in Kanagawa prefecture) of the National Vital Statistics of Japan from 2011 to 2017. The standardized mortality ratio (SMR) and Empirical Bayes estimator for the SMR (EBSMR) were used as measures. Spatial clusters were examined by Kulldorff's circular spatial scan statistic, Tango-Takahashi's flexible spatial scan statistic and Tango's test. Linear regression and conditional autoregressive (CAR) models were used not only to adjust for covariates but also to estimate regional effects. The analyses were conducted for each year, inclusive. RESULTS: Among male suicide deaths, being unemployed (50%) was most frequently related to suicide while among female health problem (50%) were frequent. Spatial clusters with significance detected by FlexScan, SatScan and Tango's test were few and varied somewhat according to the method used. Spatial clusters were detected in some regions including Kawasaki ward after adjustment by covariates. By the linear regression models, selected variables with significance were different between the sexes. For males, unemployment, family size, and proportion of higher education were detected for several of the years studied while for females, family size and divorce rate were detected over this period. These variables were also observed by the CAR model with 5 covariates. Regional effects were much clearer by considering the spatial parameter for both males and females and especially, Kawasaki ward was detected as a high risk region in many years. CONCLUSION: The present results detected some spatial clustering of suicide deaths within certain regions. Factors related to suicide deaths were also indicated. These results would provide important information in policy making for suicide prevention.

Detecting multiple spatial disease clusters: information criterion and scan statistic approach.

BACKGROUND: Detecting the geographical tendency for the presence of a disease or incident is, particularly at an early stage, a key challenge for preventing severe consequences. Given recent rapid advancements in information technologies, it is required a comprehensive framework that enables simultaneous detection of multiple spatial clusters, whether disease cases are randomly scattered or clustered around specific epicenters on a larger scale. We develop a new methodology that detects multiple spatial disease clusters and evaluates its performance compared to existing other methods. METHODS: A novel framework for spatial multiple-cluster detection is developed. The framework directly stands on the integrated bases of scan statistics and generalized linear models, adopting a new information criterion that selects the appropriate number of disease clusters. We evaluated the proposed approach using a real dataset, the hospital admission for chronic obstructive pulmonary disease (COPD) in England, and simulated data, whether the approach tends to select the correct number of clusters. RESULTS: A case study and simulation studies conducted both confirmed that the proposed method performed better compared to conventional cluster detection procedures, in terms of higher sensitivity. CONCLUSIONS: We proposed a new statistical framework that simultaneously detects and evaluates multiple disease clusters in a large study space, with high detection power compared to conventional approaches.

Spatial analysis of hypospadias cases in northern France: taking clinical data into account.

BACKGROUND: Strong evidence for a causal role of environmental factors in a congenital anomaly is still difficult to produce. The collection of statistical data is crucial for gaining a better understanding of the epidemiology and pathophysiology of these anomalies. We aimed to evaluate spatial variations in hypospadias within our region and it's association to socioeconomic and ecological factors, taking clinical data into account. METHODS: All boys with hypospadias born in northern France and seen in Lille University Medical Center (Lille, France) between 1999 and 2012 were included in the analysis. We retrospectively collected geographic data, clinical data (especially known confounding factors associated with an elevated risk of hypospadias), and demographic, socio-economic and ecological data. We analyzed the entire study population and subsequently the subset of boys lacking confounding factors. RESULTS: The study sample of 975 cases of hypospadias over the 13-year period resulted in an incidence of 25.4/10,000 male births, and was characterized by significant spatial heterogeneity (p < 0.005) and autocorrelation (p < 0.001). We detected two high-incidence clusters that differed with regard to their land use. After the exclusion of 221 patients with confounding factors, two high-incidence clusters with significant disease risks (1.65 and 1.75, respectively; p < 0.001) and a significant difference in land use (p < 0.001) again appeared. The first cluster contained a higher median [interquartile range] proportion of artificialized land (0.40 [0.22;0.47]) than the remaining "neutral areas" (0.19 [0.08;0.53]) did (p < 0.001). Conversely, the second cluster contained a higher median proportion of rural land (0.90 [0.78;0.96]) than the "neutral areas" (0.81 [0.47;0.92]) did (p < 0.001). The median deprivation index was significantly lower in the urban cluster (0.47 [0.42;0.55]) and significantly higher in the rural cluster (0.69 [0.56;0.73]) (p < 0.001). CONCLUSIONS: Our results evidenced the heterogeneous spatial distribution of cases of hypospadias in northern France. We identified two clusters with different environmental and social patterns - even after the exclusion of known confounding factors.

Detection of risk clusters for deaths due to tuberculosis specifically in areas of southern Brazil where the disease was supposedly a non-problem.

BACKGROUND: Tuberculosis (TB) is the infectious disease that kills the most people worldwide. The use of geoepidemiological techniques to demonstrate the dynamics of the disease in vulnerable communities is essential for its control. Thus, this study aimed to identify risk clusters for TB deaths and their variation over time. METHODS: This ecological study considered cases of TB deaths in residents of Londrina, Brazil between 2008 and 2015. We used standard, isotonic scan statistics for the detection of spatial risk clusters. The Poisson discrete model was adopted with the high and low rates option used for 10, 30 and 50% of the population at risk, with circular format windows and 999 replications considered the maximum cluster size. Getis-Ord Gi* (Gi*) statistics were used to diagnose hotspot areas for TB mortality. Kernel density was used to identify whether the clusters changed over time. RESULTS: For the standard version, spatial risk clusters for 10, 30 and 50% of the exposed population were 4.9 (95% CI 2.6-9.4), 3.2 (95% CI: 2.1-5.7) and 3.2 (95% CI: 2.1-5.7), respectively. For the isotonic spatial statistics, the risk clusters for 10, 30 and 50% of the exposed population were 2.8 (95% CI: 1.5-5.1), 2.7 (95% CI: 1.6-4.4), 2.2 (95% CI: 1.4-3.9), respectively. All risk clusters were located in the eastern and northern regions of the municipality. Additionally, through Gi*, hotspot areas were identified in the eastern and western regions. CONCLUSIONS: There were important risk areas for tuberculosis mortality in the eastern and northern regions of the municipality. Risk clusters for tuberculosis deaths were observed in areas where TB mortality was supposedly a non-problem. The isotonic and Gi* statistics were more sensitive for the detection of clusters in areas with a low number of cases; however, their applicability in public health is still restricted.

A spatiotemporal analysis of invasive cervical cancer incidence in the state of Maryland between 2003 and 2012.

PURPOSE: Invasive cervical cancer (ICC) rates have tremendously declined in the United States, yet new cases consistently occur in Maryland and throughout the United States. We hypothesized that although rates have generally declined, this decline is uneven across counties and over time. METHODS: Space-time cluster detection analysis was conducted to evaluate clusters of ICC incidence at the county level within Maryland between 2003 and 2012. RESULTS: The most likely cluster was a cluster of low incidence, which included 6 counties in eastern Maryland for the period 2009-2012. A secondary cluster of low rates, comprising 2 metropolitan counties in northern Maryland, was observed for the period 2009-2012. Two of the three clusters of high ICC rates occurred in 2009-2012 in the large metropolitan area of Baltimore City and another cluster in Frederick County, in rural western Maryland. The third cluster of high rates was observed 2005-2008, in western Maryland. CONCLUSION: In recent periods, some Maryland counties have experienced anomalously high or low ICC incidence. Clusters of high incidence are not explained by differences in screening rates and may be due to failures in follow-up care for cervical abnormalities that need to be investigated. Clusters of low incidence may represent areas of successful ICC control.

Sequential tests for monitoring methods to detect elevated incidence - a simulation study.

BACKGROUND: Common cancer monitoring practice is seldom prospective and rather driven by public requests. This study aims to assess the performance of a recently developed prospective cancer monitoring method and the statistical tools used, in particular the sequential probability ratio test in regard to specificity, sensitivity, observation time and heterogeneity of size of the geographical unit. METHODS: A simulation study based on a predefined selection of cancer types, geographical unit and time period was set up. Based on the population structure of Lower Saxony the mean number of cases of three diagnoses were randomly assigned to the geographical units during 2008-2012. A two-stage monitoring procedure was then executed considering the standardized incidence ratio and sequential probability ratio test. Scenarios were constructed differing by the simulation of clusters, significance level and test parameter indicating a risk to be elevated. RESULTS: Performance strongly depended on the choice of the test parameter. If the expected numbers of cases were low, the significance level was not fully exhausted. Hence, the number of false positives was lower than the chosen significance level suggested, leading to a high specificity. Sensitivity increased with the expected number of cases and the amount of risk and decreased with the size of the geographical unit. CONCLUSIONS: The procedure showed some desirable properties and is ready to use for a few settings but demands adjustments for others. Future work might consider refinements of the geographical structure. Inhomogeneous unit size could be addressed by a flexible choice of the test parameter related to the observation time.

Echovirus type 6 transmission clusters and the role of environmental surveillance in early warning, the Netherlands, 2007 to 2016.

BackgroundIn the Netherlands, echovirus type 6 (E6) is identified through clinical and environmental enterovirus surveillance (CEVS and EEVS). AimWe aimed to identify E6 transmission clusters and to assess the role of EEVS in surveillance and early warning of E6. MethodsWe included all E6 strains from CEVS and EEVS from 2007 through 2016. CEVS samples were from patients with enterovirus illness. EEVS samples came from sewage water at pre-specified sampling points. E6 strains were defined by partial VP1 sequence, month and 4-digit postcode. Phylogenetic E6 clusters were detected using pairwise genetic distances. We identified transmission clusters using a combined pairwise distance in time, place and phylogeny dimensions. ResultsE6 was identified in 157 of 3,506 CEVS clinical episodes and 92 of 1,067 EEVS samples. Increased E6 circulation was observed in 2009 and from 2014 onwards. Eight phylogenetic clusters were identified; five included both CEVS and EEVS strains. Among these, identification in EEVS did not consistently precede CEVS. One phylogenetic cluster was dominant until 2014, but genetic diversity increased thereafter. Of 14 identified transmission clusters, six included both EEVS and CEVS; in two of them, EEVS identification preceded CEVS identification. Transmission clusters were consistent with phylogenetic clusters, and with previous outbreak reports. ConclusionAlgorithms using combined time-place-phylogeny data allowed identification of clusters not detected by any of these variables alone. EEVS identified strains circulating in the population, but EEVS samples did not systematically precede clinical case surveillance, limiting EEVS usefulness for early warning in a context where E6 is endemic.

Spatiotemporal distribution and determinants of gonorrhea infections in mainland China: a panel data analysis.

OBJECTIVES: Gonorrhea remains a major public health concern worldwide. This study aims to explore the spatiotemporal distribution and sociodemographic determinants of gonorrhea rates during 2004-2014 in mainland China. STUDY DESIGN: Space-time scan statistics and spatial panel regression model. METHODS: The gonorrhea infection data and sociodemographic data during 2004-2014 at the provincial level in mainland China were extracted from the China Public Health Science Data Center and China Statistical Yearbooks, respectively. The space-time scan statistics were used to identify the high-risk clusters of gonorrhea, and the spatial panel regression model was adopted to examine the sociodemographic determinants. RESULTS: One most likely and five secondary high-risk clusters of gonorrhea rates were identified, which were mainly located in southern and eastern China. The regions with higher GDP per capita, larger floating population, less access to healthcare, higher male-female ratio, and higher divorce rate were more likely to become high-risk areas of gonorrhea. CONCLUSIONS: Gonorrhea rates were distributed unevenly through space and time and affected by various sociodemographic variables. The space-time scan statistics and spatial panel regression are viable tools for identifying clusters and examining determinants of gonorrhea rates. The findings provide valuable implications for developing targeted prevention and control programs in public health practice.

Identifying phosphorus hot spots: A spatial analysis of the phosphorus balance as a result of manure application.

In this paper, we analyze the phosphorus balance as a result of manure application on the parish level for Denmark and investigate its local geographic distribution. For our analysis, we determine phosphorus loads for the five main animal groups and the phosphorus demand of the fifteen major crop categories. Our results show that there is a large variability in the phosphorus balance within Denmark. Due to industry agglomeration statistically significant hot spots appear mainly along the west coast, while cold spots are predominantly present on southern and eastern coasts towards the Baltic Sea. The proximity of oversupply areas to water bodies and other environmentally sensitive areas reinforces the need for further phosphorus regulation. Our findings show the importance of a combined spatially targeted regulation, which allows different levels of phosphorus application depending on local economic and environmental circumstances in combination with subsidizing manure processing technologies in phosphorus hot spots.

Micro-spatial distribution of malaria cases and control strategies at ward level in Gwanda district, Matabeleland South, Zimbabwe.

BACKGROUND: Although there has been a decline in the number of malaria cases in Zimbabwe since 2010, the disease remains the biggest public health threat in the country. Gwanda district, located in Matabeleland South Province of Zimbabwe has progressed to the malaria pre-elimination phase. The aim of this study was to determine the spatial distribution of malaria incidence at ward level for improving the planning and implementation of malaria elimination in the district. METHODS: The Poisson purely spatial model was used to detect malaria clusters and their properties, including relative risk and significance levels at ward level. The geographically weighted Poisson regression (GWPR) model was used to explore the potential role and significance of environmental variables [rainfall, minimum and maximum temperature, altitude, Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), rural/urban] and malaria control strategies [indoor residual spraying (IRS) and long-lasting insecticide-treated nets (LLINs)] on the spatial patterns of malaria incidence at ward level. RESULTS: Two significant clusters (p < 0.05) of malaria cases were identified: (1) ward 24 south of Gwanda district and (2) ward 9 in the urban municipality, with relative risks of 5.583 and 4.316, respectively. The semiparametric-GWPR model with both local and global variables had higher performance based on AICc (70.882) compared to global regression (74.390) and GWPR which assumed that all variables varied locally (73.364). The semiparametric-GWPR captured the spatially non-stationary relationship between malaria cases and minimum temperature, NDVI, NDWI, and altitude at the ward level. The influence of LLINs, IRS and rural or urban did not vary and remained in the model as global terms. NDWI (positive coefficients) and NDVI (range from negative to positive coefficients) showed significant association with malaria cases in some of the wards. The IRS had a protection effect on malaria incidence as expected. CONCLUSIONS: Malaria incidence is heterogeneous even in low-transmission zones including those in pre-elimination phase. The relationship between malaria cases and NDWI, NDVI, altitude, and minimum temperature may vary at local level. The results of this study can be used in planning and implementation of malaria control strategies at district and ward levels.