Evaluating Space Time Cluster and Co-occurrence of Malaria Vectors of West Bengal in India

Evaluating Space Time Cluster and Co-occurrence of Malaria Vectors of West Bengal in India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating Space Time Cluster and Co-occurrence of Malaria Vectors of West Bengal in India Meghna Maiti, Utpal Roy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3888752/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Malaria, a prominent Vector Borne Diseases (VBDs) causing over a million annual deaths worldwide, predominantly affects vulnerable populations in the least developed regions. Despite their preventable and treatable nature, malaria remains a global public health concern. In the last decade, India has faced a significant decline in malaria morbidity and mortality. As India pledged to eliminate malaria by 2030, this study examined a decade of surveillance data to uncover space-time clustering and seasonal trends of Plasmodium vivax and falciparum malaria vectors in West Bengal. Methods Seasonal and Trend decomposition using Loess (STL) was applied to detect seasonal trend and anomaly of the time series. Univariate and multivariate space-time cluster analysis of both vectors was performed at block level using Kulldorff's space-time scan statistics from April 2011 to March 2021 to detect statistically significant space-time clusters. Results From the time series decomposition, a clear seasonal pattern is visible for both vectors. Statistical analysis indicated considerable high-risk P. vivax clusters, particularly in the northern, central, and lower Gangetic areas. Whereas, P. falciparum was concentrated in the western region with a significant recent transmission towards the lower Gangetic plan. From the multivariate space-time scan statistics, the co-occurrence of both vectors was detected with four significant clusters, which signifies the regions experiencing a greater burden of malaria vectors. Conclusions This non-random distribution underscores the urgency for dynamic monitoring and targeted interventions. Significant geographical and spatiotemporal heterogeneity was evident for both malaria vectors, emphasizing the need for tailored approaches. Identifying co-occurring clusters offers crucial insights into disease risk, paving the way for focused control initiatives. Addressing the drivers of malaria transmission in these diverse clusters demands regional cooperation and strategic strategies, crucial steps towards overcoming the final obstacles in malaria eradication. Vector borne diseases malaria STL space-time scan statistics co-occurrence. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Worldwide, vector-borne diseases (VDBs) spread by mosquitoes have resulted in a global societal deception that has killed lives and forced significant financial outlays to maintain social order. Several species of mosquitoes, which are the most frequent carriers of diseases, can cause dengue, chikungunya, malaria, and other illnesses ( 1 ). Among them, malaria is a potentially fatal infectious disease that severely affects vulnerable communities in tropical and subtropical locations where the environment is conducive to transmission. Although malaria transmission appears to be declining worldwide due to vector-borne control interventions, the 2021 estimation indicates that there are 168 million cases and 427,854 malaria deaths globally ( 2 ). In the last decade, India has faced a significant decline in malaria cases and deaths, with 1018 deaths in 2010, steeply decreased to 90 in 2021 ( 2 ). WHO Global Technical Strategy for Malaria (GTS) fixed its target to eliminate malaria globally by 2030 Asia-Pacific countries, including India, have pledged to eliminate malaria by 2030 and reducing 50% mortality rate is a mandatory goal at the global scale ( 2 ). WHO Global Technical Strategy (GTS), the Asia Pacific Leaders Malaria Alliance (APLMA), Malaria Elimination Roadmap, and the National Framework for Malaria Elimination (NFME) 2016–2030 have been developed together with partners and key stakeholders in a vision to eliminate malaria throughout the country by 2030 (NVBDCP et al., 2016). And also a target to reduce the Annual Parasite Index (API) of less than 1 by 2024 and contribute to improved health, quality of life and alleviating poverty ( 3 ). To sustain zero indigenous morbidity and mortality, newer intervention tools were implemented with the Early case Detection and Prompt Treatment (EPDT) strategy, providing Insecticide-treated bed nets (ITN) and Long-Lasting Insecticidal Nets (LLINs) to the residents for vector control, early diagnosis and prompt treatment with Artemisinin based Combination Therapy (ACT), using Indoor Residual Sprays (IRS) to protect at-risk population under Integrated Vector Management (IVM) process ( 4 ). Indian states such as Madhya Pradesh, Andhra Pradesh, Maharashtra, Bihar, West Bengal, Odisha and North East regions are highly prone to malaria endemic, contributing around 97% of total malaria cases ( 4 ). In the last decade, India has made tremendous progress in reducing malaria mortality and morbidity. Despite the steep decrease in malaria incidence across India, there are few endemic pockets where malaria remains a significant public health challenge to pose a stiff challenge to India’s malaria elimination efforts. In order to eliminate the parasite and prevent its recurrence, finding these final pockets of transmission is essential ( 5 ). In the Indian state of West Bengal, malaria is predominantly transmitted by P. vivax and is also co-endemic with P. falciparum, considered the deadliest form of malaria, varied across due to different physiographic zones, political border (interstate and international) with high-endemic malaria region. Under the NFME, West Bengal is situated in the pre-elimination phase of Category 2 with an API of less than one and one or more districts reporting an API of more than one. In 2018, the Health and Family Welfare Department of the Government of West Bengal officially declared malaria as a notifiable disease to entail on-time diagnosis and reporting by all government and private hospitals/laboratories, including non-governmental organization (NGO) run hospitals, as well as individual medical practitioners to strengthen capturing of surveillance data which is a matter concern ( 6 ). Space-time disease mapping of vector outbreaks is an informative tool for public health interventions that provide information like rate of transmission, cyclical pattern, intensity and risk of diffusion to the new location, persistent nature, etc. ( 7 , 8 ). Malaria cluster identification can aid in the demarcation of problem regions and the deployment of focused programme interventions suited for the eradication phase ( 5 ). Focused interventions in malaria-risk regions are expected to be more cost-effective than uniform resource allocation, especially in resource-constrained settings for long-term eradication programmes ( 9 – 11 ). Furthermore, knowing the seasonal pattern of malaria transmission and obtaining information of seasonal behaviour can assist in estimating the period for malaria transmission in order to initiate appropriate and effective control measures ( 12 – 14 ). Scan Statistics is one of the most common statistical methods used to identify the clusters of cases spatially and temporally ( 15 ). Whereas Kulldorff’s univariate (STSS) identifies space-time clusters of single diseases, multivariate STSS can evaluate clusters of multiple diseases that co-occurred − ( 16 , 17 ). Univariate space-time statistics have been used to identify the outbreaks and space time clusters of diseases, such as malaria ( 5 , 18 , 19 ), Dengue and Chikungunya ( 20 ), COVID 19 ( 21 ), Lyme disease ( 22 ), Chikungunya ( 23 ) and other public health problems like crime ( 24 ), deaths of despairs ( 25 ) etc. Space time scan statistics were also used to identify the cluster pattern of P. vivax and Falciparum individually in Ahmedabad City ( 26 ), Karnataka ( 27 ), Bhutan ( 5 ) etc. Whereas multivariate space time scan statistics can examine space time clusters of the simultaneous co-occurrence or co-existence of multiple diseases at one point of time used to identify outbreaks of dengue and chikungunya in Colombia ( 20 ), deaths of despairs in the US ( 25 ), etc. It is crucial to comprehend the spatio-temporal distribution of malaria vectors at the block level for developing intervention strategies since West Bengal has a porous international border with Bangladesh and a national border with high endemic states. However, no large-scale studies have examined geographic patterns of both P. vivax and P. falciparum malaria in West Bengal. This study is the first attempt to evaluate seasonal variability and the retrospective space–time distribution of individual and co-occurring - P. vivax and P. falciparum malaria across all 341 blocks in West Bengal, to the best of our knowledge. The purpose of the present study is to fill this gap in the understanding of seasonal patterns and the spatial and spatiotemporal distribution of two vectors of malaria and to estimate the relative risk of each high space time cluster at block level in West Bengal during 2011–2021. The remainder of this paper is organized as follows: Section 2 describes the geographical location of the study area and the data available for P. vivax and P. falciparum in West Bengal, and the selected methods to identify seasonal variability; significant space time clusters and evaluation of relative risk. Section 3 compares the result of the study, including exploratory analysis and seasonal pattern of malaria vectors, and compares the size and duration of space time clusters for both of the malaria vectors with their co-occurring. Finally, section 4 concludes by summarizing the main findings with a discussion and highlighting the strengths and limitations of the study and some suggested directions for further research. 2. Data Structure and Methods 2.1 Study area West Bengal is located in the eastern part of India between latitudes of 21° 31'-27° 14' N and longitudes of 85° 49'- 89° 51' E, sharing an international border with Bangladesh in the east, Bhutan and Nepal in the north. It also shares interstate boundaries with Sikkim, Assam, Jharkhand, Odisha and Bihar. It is the fourth most populous state, which occupies 88752 sq. km (34263 sq. miles) with 91 million inhabitants. This Indian state has a significant variation in climate due to its geographical diversity. The hot and dry seasons belong to the western part, and wet and cold in the northern part, including warmer conditions in the coastal areas in the southern part, are the most significant. Belonging to a monsoon climatic region, Bengal faces summer, winter and rainy seasons most significantly. 2.2 Data As malaria is a vector-borne notifiable disease, the number of positive test results have to be reported by the laboratories to the Department of Health, West Bengal, through the National Vector Borne Disease Control Programme (NVBDCP). In West Bengal, routine diagnosis of malaria is performed by rapid diagnostic test (RDT), as NVBDCP recommends, and gold microscopy is used more for confirmatory diagnosis. Malaria surveillance is carried out through the public health sub-centre, District Hospitals (DH), Block Primary Health Centre (BPHC), Primary Health Centers (PHC) and Rural Hospitals (RH). Slides are collected by Accredited Social Health Activists (ASHA), Auxiliary Nursing Midwifery (ANM) and other health workers in the community in all blocks. After slide examination through the respective diagnosis unit, all positive test reports will be submitted to the Department of Health for further analysis. A monthly report on malaria is published by the Health Management Information System (HMIS), Ministry of Health and Family Welfare, Government of India. However, a dataset containing reported positive cases of two malaria species, P. vivax and P. falciparum, has been collected for subsequent analysis for each of 341 blocks (excluding Kolkata 1 ) in West Bengal from April 2011 to March 2021. The datasets were compiled, and the cases were geocoded to the block level with the help of ESRI Arc GIS 10.8.1. Relevant population-related data are taken from the last national Census for subsequent analysis. 2.3 Methods 2.3.1 STL and Anomaly Detection STL or ‘Seasonal and Trend decomposition using Loess’ is a versatile and robust method for decomposing time series developed by Robert Cleveland and others in 1990 which uses locally fitted regression models to decompose a time series into trend, seasonal and residual or remainder components ( 28 ). With the help of two loops, the STL algorithm smoothes the time series using LOESS; the inner loop alternates between seasonal and trend smoothing, while the outer loop reduces the impact of outliers. At first, the seasonal component is calculated during the inner loop and then subtracted before the trend component is calculated. The residual is calculated by subtracting the seasonal and trend components from the time series. In this study additive decomposition was used as follows: $${Y}_{t}={T}_{t}+ {S}_{t}+{R}_{t}$$ where \({Y}_{t}\) represents the number of malaria cases with logarithmic transformation, \({T}_{t}\) is the trend component, \({S}_{t}\) is the seasonal component, \({R}_{t}\) is the residual component, for \(t\) = 1 to N (month) time. In addition, from the residual component, anomaly detection was also incorporated which deviates significantly from the normal time series, helping to detect the extreme condition of monthly observation of malaria cases. 2.3.2 Space Time Scan Statistics In this study, Kulldorff’s retrospective univariate and multivariate Space Time Scan Statistics (STSS) were applied in SaTScan™ software v.10.1( 15 ) to identify statistically significant space-time clusters and to estimate the relative risk (RR) of P. vivax and P. falciparum of malaria species on a successive 120-month period from April 2011 to March 2021. In order to identify space-time clusters of disease incidence, a cylindrical scanning window with a circular geographic base and a height that corresponds to time is placed over the research region and moves from one point to another. The age structure of the population may influence the incidence of disease, however, due to the inaccessibility of the age and sex data at this time for cases in this study, the assumption has been made that malaria incidence follows a Poisson distribution according to the block level population, e.g. the assumed population at risk. According to the null hypothesis, the model depicts an inhomogeneous Poisson process with an intensity that is proportionate to the population at risk. Whereas the alternative hypothesis is that there are more instances of malaria than would be predicted under the null model. The base of each cylinder is centred on the centroid of a particular block of the study region and expanded in both the space and time direction until a maximum spatial and temporal threshold is reached. In this study it relaxes the restriction on the cylinder end point searching where the cylinder scans each point throughout the time period to get the cluster at any time. The maximum size of the spatial window was set at 25% of the exposed population and the temporal window of the space time cylinder was set at 25% of the study period with no geographical overlapping of clusters. Also, 999 Monte Carlo replications for the testing of statistical significance at p value < 0.05 was used to ensure adequate power for defining a cluster. Mapping of the significant clusters and relative risk of univariate and multivariate P. vivax and P. falciparum were done using the GIS ArcMap 10.8.1. 3. Result 3.1 Exploratory analysis During the study period (April 2011 to March 2021), n = 55476 P. vivax cases and n = 20844 P. falciparum cases were positively reported across 341 Blocks in West Bengal. P. vivax accounted for 72.68% of malaria cases, while 27.31% were P. falciparum. Figure 1 displays the box plot with the monthly distribution of P. vivax and P. falciparum malaria cases across 341 Blocks in West Bengal during the study period, which represents the value of the interquartile range of 25th, 50th and 75th percentile and whiskers with dispersion. Though both vectors were observed throughout the year, they tended to increase from June to November, the monsoon and post-monsoon season in West Bengal. Throughout the study period, 71.15% of P. vivax cases were observed during monsoon and post-monsoon periods, while 70.24% were P. falciparum. Also, a substantial decrease in P. vivax and P. falciparum malaria cases was observed during the summer and winter seasons during the study period. The various seasons or climatic conditions play an important role in seasonal variations of malaria vector distribution. Maximum dispersion took place during the monsoon period. 3.2 Time-Series Decomposition and Anomaly Detection From the time series decomposition, a clear seasonal pattern is visible for both P. vivax (Fig. 2 A) and P. falciparum (2B) vectors. The seasonal component of STL highlighted the recurring temporal pattern (with the shape of an oscillating or wave pattern). For P. vivax, a high count occurred during the monsoon season of June and July months and a low count in January, with the oscillation decreasing narrowly over time indicating a slower rate of P. vivax occurrence. Whereas, in the case of P. falciparum, two small peaks are associated before and after the monsoon month of June and oscillation decreases in amplitude over time which indicates that the seasonal variation is decreasing over time. The inter-annual pattern showed two large peaks during mid-2012 and mid 2017 for P. vivax, and one large peak during early 2012 with a plateauing peak during early 2015 to late 2017 for P. falciparum, followed by a dropping nature afterwards. Anomalies were identified during July, August and September 2017 for P. vivax and during July for P. falciparum as an outlier located outside the threshold limit. However, the annual number of both vectors showed a considerable decrease especially after 2015. 3.3 Univariate Space Time Cluster of P. vivax The univariate space-time cluster analysis of monthly P. vivax malaria cases identified a non-random distribution of cases at the Block level in West Bengal from April 2011 to March 2021. The analysis detected seven significant clusters affecting 157 blocks for P. vivax cases (Table 1 ). A column with no of blocks represents associated blocks that form a cluster. The most observed cases (n = 5068) were found in cluster 1, centered at the Binpur-II block, with the highest relative risk (RR = 42.95) from April 2011 to November 2014, located on the western side of the study area. Three clusters were detected in 2017 centered at Kalchini (cluster 2 from June to December 2017), Magrahat-II (cluster 4 from August to October 2017) and Mangolkote (cluster 7 from July to October 2017). Cluster 3, centered at Farakka with 34 blocks, was detected as the most extended temporal cluster from April 2012 to November 2016, with the highest observed cases (n = 9184). In 2018, one cluster was detected centered at Jaypur in cluster 5, and cluster 6, centered at Pandua from June 2011 to August 2013, created a self-cluster (without any radius). Out of seven clusters, it is visible that clusters 1 and 3 have the most prolonged duration, claiming the persistent nature of P. vivax. Figure 3 (A) represents the space-time cluster of P. vivax cases, and throughout the study period, P. vivax cases varied across the study area. Right side Fig. 1 (B) depicts each block's statistically significant relative risk of P. vivax from the space-time clusters. Out of 341 blocks, only two were assigned zero relative risk due to no observed cases reported. From 156 blocks assigned to a cluster, 102 blocks reported relative risk < 1 due to lower observed than expected cases. The rest of the 54 blocks have higher than one relative risk due to the higher observed than expected cases. In comparison, three blocks reported more than ten relative risks, including Bhagawangola-II (RR = 11.11) from cluster 3, Binpur-II (RR = 22.47) and Ranibadh (RR = 31.9) from cluster 1. Table 1 Space Time Cluster of P. vivax Cluster Time Period Observed Cases Expected Cases No. of Blocks Relative Risk (RR) p value 1 April11- Nov14 5068 129.55 3 42.95 < 0.001 2 Jun17-Dec17 2025 81.67 7 25.69 < 0.001 3 April12- Nov16 9184 2945.30 34 3.54 < 0.001 4 Aug17-Oct17 1080 276.38 62 3.97 < 0.001 5 Feb18-Nov18 337 22.79 2 14.87 < 0.001 6 Jun11-Aug13 220 56.28 1 3.92 < 0.001 7 Jul17-Oct17 411 237.84 47 1.73 < 0.001 3.4 Univariate Space Time Cluster of P. falciparum The univariate space-time cluster analysis of monthly P. falciparum malaria cases identified a non-random distribution of cases at the Block level in West Bengal from April 2011 to March 2021. The analysis detected 18 significant clusters affecting 69 blocks for P. falciparum cases (Table 2 ). The most observed cases (n = 8224) found in cluster 1, centered at Barabazar block from October 2011 to September 2016, have the most extended duration cluster. Out of 18 clusters, 10 clusters form a self-centered cluster due to higher observed cases. Eight clusters were detected from 2011 to 2016, centered at Matiali (cluster 2 from July 2011 to December 2011), Kaliachak-I (cluster 3 from August 2012 to November 2012), Illambazar (cluster 4 from June 2013 to July 2013), Khoyrasol (cluster 6 during July 2011 to September 2011), Murshidabad Jiaganj (cluster 7 during July 2012 to November 2012), Potashpur- I ( cluster 9 during June 2011 to November 2012), Kolaghat (cluster 11 during October 2013 to February 2016) and Tahatta (cluster 14 during November 2014 to March 2015). Two thousand seventeen two clusters were reported, centered at Manteswar (cluster 12 from April 2017 to May 2017) and Mandirbazar (cluster 16 from August 2017 to November 2017). From 2018 to 2019, two clusters were detected, centered at Hanskhali (cluster 13 from July 2018 to February 2019) and Shyampur (cluster 17 from September 2018 to Oct 2018). From 2020 onwards, five clusters were identified with a duration between one to three months, out of which three clusters centered at Jangipara (cluster 5 from January 2020 to March Table 2 Space Time Cluster of P. falciparum Cluster Time Period Observed Cases Expected Cases No. of Blocks Relative Risk (RR) p value 1 Oct11 - Sep16 8334 603.92 31 23.07 < 0.001 2 Jul11 - Dec11 192 1.66 1 116.57 < 0.001 3 Aug12 - Nov12 183 5.68 2 32.49 < 0.001 4 Jun13 - Jul13 94 0.80 1 118.70 < 0.001 5 Jan20 - Mar20 95 1.57 1 60.89 < 0.001 6 Jul11 - Sep11 63 1.08 1 58.29 < 0.001 7 Jul12 - Nov12 172 23.89 8 7.25 < 0.001 8 Feb21 - Feb21 25 0.54 1 45.94 < 0.001 9 Jun11 - Nov12 47 7.36 1 6.40 < 0.001 10 Jan21 - Mar21 21 1.52 1 13.81 < 0.001 11 Oct13 - Feb16 64 19.84 1 3.23 < 0.001 12 April17 - May17 20 1.83 2 10.95 < 0.001 13 Jul18 - Feb19 30 5.53 1 5.43 < 0.001 14 Nov14 - May15 20 2.50 1 8.02 < 0.001 15 Jan21 - Mar21 23 5.01 3 4.60 < 0.001 16 Aug17 - Nov17 20 3.86 2 5.19 < 0.001 17 Sep18 - Oct18 23 6.90 7 3.34 < 0.001 18 Oct20 - Nov20 20 5.76 4 3.48 < 0.001 2020), Haringhata (cluster 8 from February 2021), Baruipur (cluster 18 from October 2020 to November 2020). Another two clusters centered at Ranaghat- I (cluster 10) and Moyna (cluster 15) from January 2021 to March 2021. While the highest observed cases were reported from cluster 1, cluster 4 was identified with the highest relative risk due to the lowest number of expected P. falciparum cases. Figure 4 (A) represents the space-time cluster of P. falciparum cases from April 2011 to March 2021. Right side Fig. 4 (B) depicts each block's statistically significant relative risk of P. falciparum from the space-time clusters. Out of 341 blocks, 25 blocks assigned zero relative risk due to no observed P. falciparum cases reported. Of 69 blocks assigned to a cluster, 28 blocks reported relative risk < 1 due to lower observed than expected cases. The other 41 blocks have higher than one relative risk due to the higher observed than expected cases. From cluster 1, out of 31 blocks eight blocks reported relative risk > 10 including Ranibadh (RR = 10.89), Jhalda- II (RR = 14.04), Arsha (RR = 21.35), Binpur-II (RR = 27.76), Jhalda- I (RR = 28.97), Balaramapur (RR = 30.66), Bagmundi (63.35) and Banduan (RR = 80.02). 3.5 Multivariate Space Time Cluster Table 3 summarizes the result of the multivariate space-time clusters analysis, where nine clusters detected the coexistence of P. vivax and P. falciparum, affecting 133 blocks. Clusters 1 to 3 and 5 were formed with P. vivax and P. falciparum, while clusters 4 and 9 were formed with only P. vivax and clusters 6–8 were formed with P. falciparum. Four clusters detected with P. vivax and P. falciparum centered at Balarampur (cluster 1 from April 2011 to March 2016 with 16276 observed cases), Farakka (cluster 2 from April 2012 to November 2016 with 10539 observed cases), Kalchini (cluster 3 from June 2017 to December 2017) and Jaypur (cluster 5 during February 2018 to November 2018). While Cluster 4, centered at Magrahat- II (from August 2017 to October 2017) and Cluster 9, centered at Pandua (from June 2011 to August 2013), formed with P. vivax cases only, though P. falciparum cases were observed there, the analysis could not find any statistically significant space-time cluster. An alternative scenario took place with three clusters, including Cluster 6, centered at Illambazar (from June 2013 to July 2013); Cluster 7, centered at Jangipara (from January 2020 to March 2020); and Cluster 8, centered at Khoyrasol (from July 2011 to September 2011) with P. falciparum cases only. There was no evidence to construct a significant space-time cluster in those cases. Table 3 Multivariate Space Time Cluster Cluster Time Period Vectors Observed Cases Expected Cases Relative Risk (RR) No. of Blocks p value 1 Apr11 - Mar16 PV 8665 1439.44 7.42 24 < 0.001 PF 7611 482.7 24.96 2 Apr12 - Nov16 PV 9184 2945.3 3.54 34 < 0.001 PF 1455 1053.55 1.41 3 Jun17 - Dec17 PV 2025 81.67 25.69 7 < 0.001 PF 44 29.22 1.51 4 Aug17- Oct17 PV 1080 276.38 3.97 62 < 0.001 PF NA NA NA 5 Feb18 - Nov18 PV 337 22.79 14.87 2 < 0.001 PF 9 8.15 1.1 6 Jun13 - Jul13 PV NA NA NA 1 < 0.001 PF 94 0.8 118.7 7 Jan20 - Mar20 PV NA NA NA 1 < 0.001 PF 95 1.57 60.89 8 Jul11 - Sep11 PV NA NA NA 1 < 0.001 PF 63 1.08 58.29 9 Jun11 - Aug13 PV 220 56.28 3.92 1 < 0.001 PF NA NA NA In comparison with both results, there is the presence of the same clusters in a multivariate analysis that was also detected in univariate space-time P. vivax and P. falciparum analysis. Multivariate clusters 2, 3, and 4 are the same group of clusters that contain the same blocks in univariate P. vivax clusters 3, 2 and 4, whereas multivariate cluster 1 contains a portion of P. falciparum cluster 1. Figure 5 A represents the multivariate cluster, which represents the co-occurrence of P. vivax and P. falciparum cases from April 2011 to March 2021. Figure 5 (B) depicts the statistically significant relative risk of multivariate clusters of each block from the space-time cluster analysis. In multivariate analysis, 133 blocks are combinedly affected by the co-occurrence of P. vivax and P. falciparum. Out of 133 blocks, 68 reported 1 relative risk. After all, multivariate space-time scan statistics helped to identify the co-occurrence of both P. vivax and P. falciparum. In contrast, the multivariate study highlights the regions significantly affected by both malaria vectors. Further studies are required to understand the complex spatial variation of vectors. 4. Discussion Using ten years of retrospective surveillance data, this study demonstrated seasonal variability and significant space-time clusters of P. vivax and P. falciparum malaria vectors at the block level in West Bengal during April 2011 to March 2021. Throughout the study period, seasonal occurrence of malaria vectors maximized during monsoon and post-monsoon, which follow a seasonal oscillating pattern, signify a strong relation with monsoon climate. During monsoon, favourable environmental conditions accelerate the breeding and growth of malaria vectors as well as transmission. Even malaria vectors can remain dormant for a long time until they get favourable conditions. Therefore, focused interventions are required, including monitoring and distribution of LLINs, ITN, vector control strategies with chemical and biological, community participation, environmental management, garbage cleaning, and awareness campaign should be strengthened to interrupt local transmission ( 6 ). With the help of retrospective univariate and multivariate space-time scan statistics, both analyses detected significant clusters of P. vivax and P. falciparum individually and co-occurring of both vectors. From the analysis, non-random distribution of malaria vectors has been found to create different sizes of clusters of both vectors through the study area in different periods. High-risk spatiotemporal clusters of P. vivax were detected across cross border area in 34 blocks from Maldah, Murshidabad and Birbhum districts located at the central part of West Bengal along with Bangladesh and Jharkhand border. Another high-risk spatiotemporal cluster of P. vivax is found in northern part along with Bhutan and Assam border. Although, these areas are considering as porous border with Bangladesh which act as cross border illegal migration (Fambirai et al., 2022; Gupta et al., 2022). Therefore, cross border population movement and sharing a long international border are likely to be a constant danger of influx of malaria cases from high-endemic to low-endemic areas (Penjor et al., 2023; WHO, 2019). High risk spatiotemporal clusters of P. falciparum were observed in the western part of West Bengal with Puruliya, Bankura, Jhargram districts covered by forest area. This cluster also identified as the most prolonged time high-risk zone before 2017 and persisted over five years, contributing more than 85% P. falciparum cases. Dense forests, hills, perennial streams and high tribal and marginalised population groups living in improvised conditions ( 33 ) potentially contribute to this situation. The risk of malaria transmission across these regions are quite high as local population living in the forest fringe and tribal areas which is physio-graphically mosquito-prone area. However, prevalence of malaria cases decreased in Puruliya district from 2017 onwards which is possible with proper intervention and distribution of LLINs on time ( 34 ). The present study also identified expansion in the geographical distribution of P. falciparum over P. vivax in the southern part of the Gangetic plain, especially after 2017. The emergence of P. Falciparum clusters in the newer regions requires targeted interventions to disrupt further transmission. As the elimination process is progressing to be malaria-free by 2027 and to eliminate the disease by 2030, the road ahead is bumpy and beset with many challenges, including the emergence of a set of new asymptomatic or sub-microscopic malaria parasitaemia ( 35 – 38 ), the emergence of Artemisinin and multidrug-resistant malaria ( 39 , 40 ). Likewise, the elimination of malaria fever incidence and measuring transmissivity requires a strong surveillance system, which helps to detect infection early and enables a rapid and effective response. From the analysis distinct patterns were seen in the spatial variations between P. vivax and P. falciparum malaria, indicating that the biological features of the parasites varied and might have played a role in their transmission. The activity of vectors and the length of parasite incubation are influenced by climate, and these factors are associated with a higher risk of malaria ( 41 ). P. falciparum needs a slightly higher temperature for parasite growth than P. vivax. As for both vectors, the minimal threshold temperature is around 18 and 15°C, respectively; there is a greater chance that P. falciparum may spread to formerly colder locations due to global climate change ( 42 ). Further investigation is required to fully understand the risk factors driving the geographic distribution and spatial shifting of P. falciparum throughout West Bengal. Furthermore, the univariate study independently examines both vectors' spatial and temporal variation and highlights the burden of P. vivax and P. falciparum. In contrast, the multivariate study highlights the regions significantly affected by both malaria vectors. However, the co-occurrence of both vectors in four high-risk clusters signifies the transmission dynamics, though no clinical or microscopic coinfection has been recorded. Four multivariate clusters with P. vivax and P. falciparum were observed from April 2011 to November 2018. The coexistence of both vectors in the same geographical region within the same climatic condition indicates that seasonal factors may influence the co-occurrence, giving a call for vector control monitoring. Given the frequent coexistence of P. vivax and P. falciparum in India, prioritizing intervention actions becomes challenging due to potential variations in disease transmission between these species ( 43 , 44 ). As the objective of this is to identify the existence of space-time clusters and seasonal variability of malaria vectors, consequent studies are required to identify determining factors associated with this co-occurrence. However, this study has a variety of limitations. First, for this study block level administrative unit has been taken but small scale like village level unit will help better understanding for disease outbreak and surveillance. Second important limitation is relative risk reported by each cluster. Reported relative risk for each cluster was detected underlying total population, which does not consider sociodemographic variation. In areas of low transmission all age group are vulnerable but adults develop more severe and multiple complications. In areas of high transmission children below 5 years, visitors and migratory labour are higher at risk. Pregnant women are less capable of coping with and clearing malaria infection, adversely affecting the unborn fetus. Therefore, adjusted rate based on age, sex and other socio-economic parameters might generate accurate relative risk. Third, though SaTScan is an important statistical tool for spatial and space time cluster analysis, challenging to determine the maximum window limit for spatial and temporal extension, as a result misdiagnosis of small and heterogenous clusters. Last, block level population data was taken from last census occurred in 2011. Using outdated demographic data is a critical issue effecting the findings of the research and a common limitation in many studies in developing countries. Despite having various limitations, this study is a first attempt to examine the seasonal variability and spatio-temporal cluster of malaria vectors and its cooccurrence at the block level using 120 months longitudinal dataset. Identifying the covariate effects and transmission in the spatiotemporal context, ecological modeling like autoregressive study is required which will avenue the future target. 5. Conclusion From the purview of spatiotemporal extent, the space-time cluster analysis shows a robust spatiotemporal relationship between malaria vector prevalence in West Bengal. This study found significant univariate and multivariate space-time clusters of P. vivax and P. falciparum malaria vectors, as well as their co-occurrence and seasonal fluctuation. The detected clusters and the relative risk of each cluster were also visualized to improve the understanding of the malaria epidemic throughout the study area. As NVBDCP is approaching the elimination procedure by targeting to be malaria-free by 2027 and to eliminate the disease by 2030, disease prevention and control lending community support are much needed along with following up cross-border migration, supplying LLINs and IRS, and the advent of ACTs and application of more sensitive tools like polymerase chain reaction (PCR) and Loop-mediated isothermal amplification (LAMP) with conventional methods to overcome the challenge of malaria elimination. Addressing the determinants of malaria transmission in these varied clusters necessitates regional collaboration and strategic plans, which are critical steps towards overcoming the remaining hurdles in malaria eradication. Abbreviations VBDs Vector Borne Diseases GTS Global Technical Strategy APLMA Asia Pacific Leaders Malaria Alliance NFME National Framework for Malaria Elimination EPDT Early case Detection and Prompt Treatment NVBDCP National Vector Borne Disease Control Programme ITN Insecticide-treated bed nets LLIN Long-Lasting Insecticidal Nets ACT Artemisinin based Combination Therapy IRS Indoor Residual Sprays IVM Integrated Vector Management STSS Space Time Scan Statistics RR Relative Risk PCR Polymerase Chain Reaction LAMP Loop-mediated isothermal amplification Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials All data analysed in this study was taken from the Ministry of Health and Family Welfare, Government of India. Competing interests The authors declare that they have no competing interests. Funding There has been no significant financial support for this work that could have influenced its outcome. Authors' contributions MM developed methodology, performed analysis, interpreted the outcome and prepared the original draft. UR supervised the investigation and reviewed the final draft. All authors read and approved the final manuscript. Acknowledgements Not applicable References WHO. Vector-borne diseases [Internet]. 2020 [cited 2023 Jul 27]. Available from: https://www.who.int/news-room/fact-sheets/detail/vector-borne-diseases WHO. World malaria report 2022 [Internet]. Geneva; 2022 [cited 2023 Jul 26]. Available from: https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2022 NVBDCP G, DGHS, MFHW. National Framework forMalaria Elimination in India (2016-2030) [Internet]. India; 2016. Available from: https://nvbdcp.gov.in/WriteReadData/l892s/National-framework-for-malaria-elimination-in-India-2016%E2%80%932030.pdf NVBDCP. National Vector Borne Disease Control Programme [Internet]. 2021 [cited 2019 May 7]. Available from: https://www.nvbdcp.gov.in/ Wangdi K, Penjor K, Tobgyal, Lawpoolsri S, Price RN, Gething PW, et al. Space–Time Clustering Characteristics of Malaria in Bhutan at the End Stages of Elimination. IJERPH. 2021 May 22;18(11):5553. HFWD. State Vector Borne Diseases Control and Seasonal Influenza Plan, 2018 [Internet]. Health & Family Welfare Department, Government of West Bengal; 2018. Available from: https://www.wbhealth.gov.in/uploaded_files/ticker/State_Vector_Borne_2018.pdf Hay SI, Battle KE, Pigott DM, Smith DL, Moyes CL, Bhatt S, et al. Global mapping of infectious disease. Philosophical Transactions of the Royal Society B: Biological Sciences. 2013 Feb 4;368(1614):20120250–20120250. Kulldorff M, Nagarwalla N. Spatial disease clusters: detection and inference. Stat Med. 1995 Apr 30;14(8):799–810. Clements ACA, Reid HL, Kelly GC, Hay SI. Further shrinking the malaria map: how can geospatial science help to achieve malaria elimination? Lancet Infect Dis. 2013 Aug;13(8):709–18. Coleman M, Coleman M, Mabuza AM, Kok G, Coetzee M, Durrheim DN. Using the SaTScan method to detect local malaria clusters for guiding malaria control programmes. Malaria Journal. 2009 Apr 17;8(1):68. Wangdi K, Pasaribu AP, Clements ACA. Addressing hard-to-reach populations for achieving malaria elimination in the Asia Pacific Malaria Elimination Network countries. Asia & the Pacific Policy Studies. 2021;8(2):176–88. Ikeda T, Behera SK, Morioka Y, Minakawa N, Hashizume M, Tsuzuki A, et al. Seasonally lagged effects of climatic factors on malaria incidence in South Africa. Sci Rep. 2017 May 29;7(1):2458. Nguyen M, Howes RE, Lucas TCD, Battle KE, Cameron E, Gibson HS, et al. Mapping malaria seasonality in Madagascar using health facility data. BMC Medicine. 2020 Feb 10;18(1):26. Stuckey EM, Smith T, Chitnis N. 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Spatial and space–time distribution of Plasmodium vivax and Plasmodium falciparum malaria in China, 2005–2014. Malaria Journal. 2016 Dec 19;15(1):595. Desjardins MR, Whiteman A, Casas I, Delmelle E. Space-time clusters and co-occurrence of chikungunya and dengue fever in Colombia from 2015 to 2016. Acta Tropica. 2018 Sep;185:77–85. Xu F, Beard K. A comparison of prospective space-time scan statistics and spatiotemporal event sequence based clustering for COVID-19 surveillance. PLOS ONE. 2021 Jun 10;16(6):e0252990. Li J, Kolivras KN, Hong Y, Duan Y, Seukep SE, Prisley SP, et al. Spatial and temporal emergence pattern of Lyme disease in Virginia. Am J Trop Med Hyg. 2014 Dec;91(6):1166–72. Nsoesie EO, Ricketts RP, Brown HE, Fish D, Durham DP, Mbah MLN, et al. Spatial and Temporal Clustering of Chikungunya Virus Transmission in Dominica. PLOS Neglected Tropical Diseases. 2015 Aug 14;9(8):e0003977. Nakaya T, Yano K. Visualising Crime Clusters in a Space-time Cube: An Exploratory Data-analysis Approach Using Space-time Kernel Density Estimation and Scan Statistics. Transactions in GIS. 2010;14(3):223–39. Steelesmith DL, Lindstrom MR, Le HTK, Root ED, Campo JV, Fontanella CA. Spatiotemporal Patterns of Deaths of Despair Across the U.S., 2000-2019. Am J Prev Med. 2023 Aug;65(2):192–200. Parizo J, Sturrock HJW, Dhiman RC, Greenhouse B. Spatiotemporal Analysis of Malaria in Urban Ahmedabad (Gujarat), India: Identification of Hot Spots and Risk Factors for Targeted Intervention. Am J Trop Med Hyg. 2016 Sep 7;95(3):595–603. Shekhar S, Yoo EH, Ahmed SA, Haining R, Kadannolly S. Analysing malaria incidence at the small area level for developing a spatial decision support system: A case study in Kalaburagi, Karnataka, India. Spatial and Spatio-temporal Epidemiology. 2017 Feb 1;20:9–25. Cleveland RB, Cleveland WS, McRae JE, Terpenning I. STL: A Seasonal-Trend Decomposition Procedure Based on Loess. Journal of Official Statistics. 1990;6(1):3–73. Fambirai T, Chimbari MJ, Ndarukwa P. Global Cross-Border Malaria Control Collaborative Initiatives: A Scoping Review. Int J Environ Res Public Health. 2022 Sep 26;19(19):12216. Gupta SK, Saroha P, Singh K, Saxena R, Barman K, Kumar A, et al. Malaria Epidemiology Along the Indian Districts Bordering Bhutan and Implications for Malaria Elimination in the Region. Am J Trop Med Hyg. 2022 Feb;106(2):655–60. Penjor K, Zangpo U, Tshering D, Ley B, Price RN, Wangdi K. Imported malaria and its implication to achievement of malaria-free Bhutan. Journal of Travel Medicine. 2023 Apr 1;30(3):taad044. WHO. Meeting on cross border collaboration on malaria elimination along the India- Bhutan border [Internet]. Guwahati, Assam, India: World Health Organization, South-East Asia; 2019. Available from: https://www.who.int/docs/default-source/searo/malaria/cross-border-malaria-meeting-report.pdf?sfvrsn=85dbe22c_2 Anvikar A, Dev V. Malaria transmission in India: disease distribution and prevalence of mosquito vectors in different physiographic zones. In: Vector Biology and Control: An Update for Malaria Elimination Initiative in India [Internet]. New Delhi, India: The National Academy of Sciences (NASI); 2020. p. 117–28. Available from: https://nimr.org.in/images/pdf/BCIL_vector.pdf Pradhan S, Hore S, Maji SK, Manna S, Maity A, Kundu PK, et al. Study of epidemiological behaviour of malaria and its control in the Purulia district of West Bengal, India (2016-2020). Sci Rep. 2022 Jan 12;12(1):630. Deora N, Yadav CP, Pande V, Sinha A. A systematic review and meta-analysis on sub-microscopic Plasmodium infections in India: Different perspectives and global challenges. The Lancet Regional Health - Southeast Asia [Internet]. 2022 Jul 1 [cited 2023 Aug 31];2. Available from: https://www.thelancet.com/journals/lansea/article/PIIS2772-3682(22)00012-9/fulltext#%20 Kaura T, Kaur J, Sharma A, Dhiman A, Pangotra M, Upadhyay AK, et al. Prevalence of submicroscopic malaria in low transmission state of Punjab: A potential threat to malaria elimination. J Vector Borne Dis. 2019;56(1):78–84. Kumari P, Sinha S, Gahtori R, Yadav CP, Pradhan MM, Rahi M, et al. Prevalence of Asymptomatic Malaria Parasitemia in Odisha, India: A Challenge to Malaria Elimination. The American Journal of Tropical Medicine and Hygiene. 2020 Oct 7;103(4):1510–6. van Eijk AM, Sutton PL, Ramanathapuram L, Sullivan SA, Kanagaraj D, Priya GSL, et al. The burden of submicroscopic and asymptomatic malaria in India revealed from epidemiology studies at three varied transmission sites in India. Sci Rep. 2019 Nov 19;9:17095. Choubey D, Deshmukh B, Rao AG, Kanyal A, Hati AK, Roy S, et al. Genomic analysis of Indian isolates of Plasmodium falciparum: Implications for drug resistance and virulence factors. International Journal for Parasitology: Drugs and Drug Resistance. 2023 Aug 1;22:52–60. Dhorda M, Amaratunga C, Dondorp AM. Artemisinin and multidrug-resistant Plasmodium falciparum – a threat for malaria control and elimination. Curr Opin Infect Dis. 2021 Oct;34(5):432–9. Wang D, Li S, Cheng Z, Xiao N, Cotter C, Hwang J, et al. Transmission Risk from Imported Plasmodium vivax Malaria in the China–Myanmar Border Region. Emerg Infect Dis. 2015 Oct;21(10):1861–4. Patz JA, Olson SH. Malaria risk and temperature: Influences from global climate change and local land use practices. Proc Natl Acad Sci U S A. 2006 Apr 11;103(15):5635–6. Dhiman RC, Chavan L, Pant M, Pahwa S. National and regional impacts of climate change on malaria by 2030. Current Science. 2011;101(3):372–83. Bhattacharya S, Sharma C, Dhiman R, Mitra A. Climate change and malaria in India. Curr Sci India. 2005 Nov 30;90. Footnotes Kolkata, the urban metropolitan city and also district in West Bengal, is also a malaria burden zone with API > 1. For this study, Kolkata has been excluded for spatio-temporal analysis due to unavailability of case data during the time frame. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3888752","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269081812,"identity":"7321d0f8-fb63-4c80-a927-1f24c4a4386a","order_by":0,"name":"Meghna Maiti","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYFAD9gYGhgcMDDwQngExWngOMDAkkKZFIgGshTDQnXb84oMPvxjy5Wc+fvghse2OjMEB5ocfGAru4NRidjun2HBmH4PlhttpxhKJbc94DA6wGUswGDzDpyVNmreHwcBAOocBqOUwj2QDgxnQL4fxaUn/DdIiP/MM8w+IFvZvBLSkH2Pm+QEMoRs8bGBb+Bl4CNrCLDmzAeiwM2lmFgnngFqYeYolEvDb8vDDhz9Ah7UffnzjQ9lhezb29o1AEdxagHFnwMDY9h9JgJmBUASxP2Bg+INXxSgYBaNgFIx0AACKnlItKp2cCwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Calcutta","correspondingAuthor":true,"prefix":"","firstName":"Meghna","middleName":"","lastName":"Maiti","suffix":""},{"id":269081813,"identity":"c7b73f9f-f1e1-4fc8-acd2-b131d767d573","order_by":1,"name":"Utpal Roy","email":"","orcid":"","institution":"University of Calcutta","correspondingAuthor":false,"prefix":"","firstName":"Utpal","middleName":"","lastName":"Roy","suffix":""}],"badges":[],"createdAt":"2024-01-22 18:44:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3888752/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3888752/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50175493,"identity":"a48ea9ff-9879-4068-aef4-226b95857535","added_by":"auto","created_at":"2024-01-25 16:21:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11131,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly distribution of P. vivax and P. falciparum malaria vectors in West Bengal during the study period\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3888752/v1/a8a6599214315560999f51ce.png"},{"id":50175494,"identity":"8d138b76-b403-43ad-9fd8-3ce7c88dec65","added_by":"auto","created_at":"2024-01-25 16:21:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":212359,"visible":true,"origin":"","legend":"\u003cp\u003eDecomposed Time-Series and Anomaly Detection of (A)P. vivax and (B) P. falciparum cases\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3888752/v1/b6d7421bab19c6d1d8316a8a.png"},{"id":50175497,"identity":"05d44c82-4a77-45c6-a1cd-302286eed8aa","added_by":"auto","created_at":"2024-01-25 16:21:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":562306,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Space-Time Cluster and (B) Statistically Significant Relative Risk of P. vivax\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3888752/v1/71606cdb87d4dd44325e591b.png"},{"id":50175496,"identity":"15a10538-71d0-4437-a67a-5610f33c3576","added_by":"auto","created_at":"2024-01-25 16:21:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":579712,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Space-Time Cluster and (B) Statistically Significant Relative Risk of P. falciparum\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3888752/v1/7ef35c7056a7c89458426922.png"},{"id":50176071,"identity":"7f60fc94-e8e8-4c74-a1da-54ba5c2c5bd3","added_by":"auto","created_at":"2024-01-25 16:29:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":490477,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Multivariate Space-Time Cluster and (B) Statistically Significant Relative Risk\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3888752/v1/3d6047f533efddbd33372767.png"},{"id":50176266,"identity":"9f55f9fc-f9dd-4ef6-8fe4-2400ab927ed0","added_by":"auto","created_at":"2024-01-25 16:37:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2119392,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3888752/v1/9f85c584-7d95-4b1f-a0ae-8d6ed030cb2a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating Space Time Cluster and Co-occurrence of Malaria Vectors of West Bengal in India","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWorldwide, vector-borne diseases (VDBs) spread by mosquitoes have resulted in a global societal deception that has killed lives and forced significant financial outlays to maintain social order. Several species of mosquitoes, which are the most frequent carriers of diseases, can cause dengue, chikungunya, malaria, and other illnesses (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Among them, malaria is a potentially fatal infectious disease that severely affects vulnerable communities in tropical and subtropical locations where the environment is conducive to transmission. Although malaria transmission appears to be declining worldwide due to vector-borne control interventions, the 2021 estimation indicates that there are 168\u0026nbsp;million cases and 427,854 malaria deaths globally (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In the last decade, India has faced a significant decline in malaria cases and deaths, with 1018 deaths in 2010, steeply decreased to 90 in 2021 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). WHO Global Technical Strategy for Malaria (GTS) fixed its target to eliminate malaria globally by 2030 Asia-Pacific countries, including India, have pledged to eliminate malaria by 2030 and reducing 50% mortality rate is a mandatory goal at the global scale (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). WHO Global Technical Strategy (GTS), the Asia Pacific Leaders Malaria Alliance (APLMA), Malaria Elimination Roadmap, and the National Framework for Malaria Elimination (NFME) 2016\u0026ndash;2030 have been developed together with partners and key stakeholders in a vision to eliminate malaria throughout the country by 2030 (NVBDCP et al., 2016). And also a target to reduce the Annual Parasite Index (API) of less than 1 by 2024 and contribute to improved health, quality of life and alleviating poverty (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). To sustain zero indigenous morbidity and mortality, newer intervention tools were implemented with the Early case Detection and Prompt Treatment (EPDT) strategy, providing Insecticide-treated bed nets (ITN) and Long-Lasting Insecticidal Nets (LLINs) to the residents for vector control, early diagnosis and prompt treatment with Artemisinin based Combination Therapy (ACT), using Indoor Residual Sprays (IRS) to protect at-risk population under Integrated Vector Management (IVM) process (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndian states such as Madhya Pradesh, Andhra Pradesh, Maharashtra, Bihar, West Bengal, Odisha and North East regions are highly prone to malaria endemic, contributing around 97% of total malaria cases (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In the last decade, India has made tremendous progress in reducing malaria mortality and morbidity. Despite the steep decrease in malaria incidence across India, there are few endemic pockets where malaria remains a significant public health challenge to pose a stiff challenge to India\u0026rsquo;s malaria elimination efforts. In order to eliminate the parasite and prevent its recurrence, finding these final pockets of transmission is essential (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In the Indian state of West Bengal, malaria is predominantly transmitted by P. vivax and is also co-endemic with P. falciparum, considered the deadliest form of malaria, varied across due to different physiographic zones, political border (interstate and international) with high-endemic malaria region. Under the NFME, West Bengal is situated in the pre-elimination phase of Category 2 with an API of less than one and one or more districts reporting an API of more than one. In 2018, the Health and Family Welfare Department of the Government of West Bengal officially declared malaria as a notifiable disease to entail on-time diagnosis and reporting by all government and private hospitals/laboratories, including non-governmental organization (NGO) run hospitals, as well as individual medical practitioners to strengthen capturing of surveillance data which is a matter concern (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpace-time disease mapping of vector outbreaks is an informative tool for public health interventions that provide information like rate of transmission, cyclical pattern, intensity and risk of diffusion to the new location, persistent nature, etc. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Malaria cluster identification can aid in the demarcation of problem regions and the deployment of focused programme interventions suited for the eradication phase (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Focused interventions in malaria-risk regions are expected to be more cost-effective than uniform resource allocation, especially in resource-constrained settings for long-term eradication programmes (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Furthermore, knowing the seasonal pattern of malaria transmission and obtaining information of seasonal behaviour can assist in estimating the period for malaria transmission in order to initiate appropriate and effective control measures (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eScan Statistics is one of the most common statistical methods used to identify the clusters of cases spatially and temporally (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Whereas Kulldorff\u0026rsquo;s univariate (STSS) identifies space-time clusters of single diseases, multivariate STSS can evaluate clusters of multiple diseases that co-occurred \u0026minus;\u0026thinsp;(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Univariate space-time statistics have been used to identify the outbreaks and space time clusters of diseases, such as malaria (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), Dengue and Chikungunya (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), COVID 19 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), Lyme disease (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), Chikungunya (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) and other public health problems like crime (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), deaths of despairs (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) etc. Space time scan statistics were also used to identify the cluster pattern of P. vivax and Falciparum individually in Ahmedabad City (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), Karnataka (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), Bhutan (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) etc. Whereas multivariate space time scan statistics can examine space time clusters of the simultaneous co-occurrence or co-existence of multiple diseases at one point of time used to identify outbreaks of dengue and chikungunya in Colombia (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), deaths of despairs in the US (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), etc.\u003c/p\u003e \u003cp\u003eIt is crucial to comprehend the spatio-temporal distribution of malaria vectors at the block level for developing intervention strategies since West Bengal has a porous international border with Bangladesh and a national border with high endemic states. However, no large-scale studies have examined geographic patterns of both P. vivax and P. falciparum malaria in West Bengal. This study is the first attempt to evaluate seasonal variability and the retrospective space\u0026ndash;time distribution of individual and co-occurring - P. vivax and P. falciparum malaria across all 341 blocks in West Bengal, to the best of our knowledge. The purpose of the present study is to fill this gap in the understanding of seasonal patterns and the spatial and spatiotemporal distribution of two vectors of malaria and to estimate the relative risk of each high space time cluster at block level in West Bengal during 2011\u0026ndash;2021.\u003c/p\u003e \u003cp\u003eThe remainder of this paper is organized as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e describes the geographical location of the study area and the data available for P. vivax and P. falciparum in West Bengal, and the selected methods to identify seasonal variability; significant space time clusters and evaluation of relative risk. Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3\u003c/span\u003e compares the result of the study, including exploratory analysis and seasonal pattern of malaria vectors, and compares the size and duration of space time clusters for both of the malaria vectors with their co-occurring. Finally, section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e4\u003c/span\u003e concludes by summarizing the main findings with a discussion and highlighting the strengths and limitations of the study and some suggested directions for further research.\u003c/p\u003e"},{"header":"2. Data Structure and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eWest Bengal is located in the eastern part of India between latitudes of 21\u0026deg; 31'-27\u0026deg; 14' N and longitudes of 85\u0026deg; 49'- 89\u0026deg; 51' E, sharing an international border with Bangladesh in the east, Bhutan and Nepal in the north. It also shares interstate boundaries with Sikkim, Assam, Jharkhand, Odisha and Bihar. It is the fourth most populous state, which occupies 88752 sq. km (34263 sq. miles) with 91\u0026nbsp;million inhabitants. This Indian state has a significant variation in climate due to its geographical diversity. The hot and dry seasons belong to the western part, and wet and cold in the northern part, including warmer conditions in the coastal areas in the southern part, are the most significant. Belonging to a monsoon climatic region, Bengal faces summer, winter and rainy seasons most significantly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data\u003c/h2\u003e \u003cp\u003eAs malaria is a vector-borne notifiable disease, the number of positive test results have to be reported by the laboratories to the Department of Health, West Bengal, through the National Vector Borne Disease Control Programme (NVBDCP). In West Bengal, routine diagnosis of malaria is performed by rapid diagnostic test (RDT), as NVBDCP recommends, and gold microscopy is used more for confirmatory diagnosis. Malaria surveillance is carried out through the public health sub-centre, District Hospitals (DH), Block Primary Health Centre (BPHC), Primary Health Centers (PHC) and Rural Hospitals (RH). Slides are collected by Accredited Social Health Activists (ASHA), Auxiliary Nursing Midwifery (ANM) and other health workers in the community in all blocks. After slide examination through the respective diagnosis unit, all positive test reports will be submitted to the Department of Health for further analysis. A monthly report on malaria is published by the Health Management Information System (HMIS), Ministry of Health and Family Welfare, Government of India. However, a dataset containing reported positive cases of two malaria species, P. vivax and P. falciparum, has been collected for subsequent analysis for each of 341 blocks (excluding Kolkata\u003csup\u003e1\u003c/sup\u003e) in West Bengal from April 2011 to March 2021. The datasets were compiled, and the cases were geocoded to the block level with the help of ESRI Arc GIS 10.8.1. Relevant population-related data are taken from the last national Census for subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Methods\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 STL and Anomaly Detection\u003c/h2\u003e \u003cp\u003eSTL or \u0026lsquo;Seasonal and Trend decomposition using Loess\u0026rsquo; is a versatile and robust method for decomposing time series developed by Robert Cleveland and others in 1990 which uses locally fitted regression models to decompose a time series into trend, seasonal and residual or remainder components (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). With the help of two loops, the STL algorithm smoothes the time series using LOESS; the inner loop alternates between seasonal and trend smoothing, while the outer loop reduces the impact of outliers. At first, the seasonal component is calculated during the inner loop and then subtracted before the trend component is calculated. The residual is calculated by subtracting the seasonal and trend components from the time series. In this study additive decomposition was used as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${Y}_{t}={T}_{t}+ {S}_{t}+{R}_{t}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{t}\\)\u003c/span\u003e\u003c/span\u003e represents the number of malaria cases with logarithmic transformation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({T}_{t}\\)\u003c/span\u003e\u003c/span\u003eis the trend component, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{t}\\)\u003c/span\u003e\u003c/span\u003e is the seasonal component, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}_{t}\\)\u003c/span\u003e\u003c/span\u003eis the residual component, for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(t\\)\u003c/span\u003e\u003c/span\u003e = 1 to N (month) time. In addition, from the residual component, anomaly detection was also incorporated which deviates significantly from the normal time series, helping to detect the extreme condition of monthly observation of malaria cases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Space Time Scan Statistics\u003c/h2\u003e \u003cp\u003eIn this study, Kulldorff\u0026rsquo;s retrospective univariate and multivariate Space Time Scan Statistics (STSS) were applied in SaTScan\u0026trade; software v.10.1(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) to identify statistically significant space-time clusters and to estimate the relative risk (RR) of P. vivax and P. falciparum of malaria species on a successive 120-month period from April 2011 to March 2021. In order to identify space-time clusters of disease incidence, a cylindrical scanning window with a circular geographic base and a height that corresponds to time is placed over the research region and moves from one point to another. The age structure of the population may influence the incidence of disease, however, due to the inaccessibility of the age and sex data at this time for cases in this study, the assumption has been made that malaria incidence follows a Poisson distribution according to the block level population, e.g. the assumed population at risk. According to the null hypothesis, the model depicts an inhomogeneous Poisson process with an intensity that is proportionate to the population at risk. Whereas the alternative hypothesis is that there are more instances of malaria than would be predicted under the null model.\u003c/p\u003e \u003cp\u003eThe base of each cylinder is centred on the centroid of a particular block of the study region and expanded in both the space and time direction until a maximum spatial and temporal threshold is reached. In this study it relaxes the restriction on the cylinder end point searching where the cylinder scans each point throughout the time period to get the cluster at any time. The maximum size of the spatial window was set at 25% of the exposed population and the temporal window of the space time cylinder was set at 25% of the study period with no geographical overlapping of clusters. Also, 999 Monte Carlo replications for the testing of statistical significance at p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used to ensure adequate power for defining a cluster. Mapping of the significant clusters and relative risk of univariate and multivariate P. vivax and P. falciparum were done using the GIS ArcMap 10.8.1.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Exploratory analysis\u003c/h2\u003e \u003cp\u003eDuring the study period (April 2011 to March 2021), n\u0026thinsp;=\u0026thinsp;55476 P. vivax cases and n\u0026thinsp;=\u0026thinsp;20844 P. falciparum cases were positively reported across 341 Blocks in West Bengal. P. vivax accounted for 72.68% of malaria cases, while 27.31% were P. falciparum. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the box plot with the monthly distribution of P. vivax and P. falciparum malaria cases across 341 Blocks in West Bengal during the study period, which represents the value of the interquartile range of 25th, 50th and 75th percentile and whiskers with dispersion. Though both vectors were observed throughout the year, they tended to increase from June to November, the monsoon and post-monsoon season in West Bengal. Throughout the study period, 71.15% of P. vivax cases were observed during monsoon and post-monsoon periods, while 70.24% were P. falciparum. Also, a substantial decrease in P. vivax and P. falciparum malaria cases was observed during the summer and winter seasons during the study period. The various seasons or climatic conditions play an important role in seasonal variations of malaria vector distribution. Maximum dispersion took place during the monsoon period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Time-Series Decomposition and Anomaly Detection\u003c/h2\u003e \u003cp\u003eFrom the time series decomposition, a clear seasonal pattern is visible for both P. vivax (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and P. falciparum (2B) vectors. The seasonal component of STL highlighted the recurring temporal pattern (with the shape of an oscillating or wave pattern). For P. vivax, a high count occurred during the monsoon season of June and July months and a low count in January, with the oscillation decreasing narrowly over time indicating a slower rate of P. vivax occurrence. Whereas, in the case of P. falciparum, two small peaks are associated before and after the monsoon month of June and oscillation decreases in amplitude over time which indicates that the seasonal variation is decreasing over time. The inter-annual pattern showed two large peaks during mid-2012 and mid 2017 for P. vivax, and one large peak during early 2012 with a plateauing peak during early 2015 to late 2017 for P. falciparum, followed by a dropping nature afterwards. Anomalies were identified during July, August and September 2017 for P. vivax and during July for P. falciparum as an outlier located outside the threshold limit. However, the annual number of both vectors showed a considerable decrease especially after 2015.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Univariate Space Time Cluster of P. vivax\u003c/h2\u003e \u003cp\u003eThe univariate space-time cluster analysis of monthly P. vivax malaria cases identified a non-random distribution of cases at the Block level in West Bengal from April 2011 to March 2021. The analysis detected seven significant clusters affecting 157 blocks for P. vivax cases (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A column with no of blocks represents associated blocks that form a cluster. The most observed cases (n\u0026thinsp;=\u0026thinsp;5068) were found in cluster 1, centered at the Binpur-II block, with the highest relative risk (RR\u0026thinsp;=\u0026thinsp;42.95) from April 2011 to November 2014, located on the western side of the study area. Three clusters were detected in 2017 centered at Kalchini (cluster 2 from June to December 2017), Magrahat-II (cluster 4 from August to October 2017) and Mangolkote (cluster 7 from July to October 2017). Cluster 3, centered at Farakka with 34 blocks, was detected as the most extended temporal cluster from April 2012 to November 2016, with the highest observed cases (n\u0026thinsp;=\u0026thinsp;9184). In 2018, one cluster was detected centered at Jaypur in cluster 5, and cluster 6, centered at Pandua from June 2011 to August 2013, created a self-cluster (without any radius). Out of seven clusters, it is visible that clusters 1 and 3 have the most prolonged duration, claiming the persistent nature of P. vivax.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(A) represents the space-time cluster of P. vivax cases, and throughout the study period, P. vivax cases varied across the study area. Right side Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(B) depicts each block's statistically significant relative risk of P. vivax from the space-time clusters. Out of 341 blocks, only two were assigned zero relative risk due to no observed cases reported. From 156 blocks assigned to a cluster, 102 blocks reported relative risk\u0026thinsp;\u0026lt;\u0026thinsp;1 due to lower observed than expected cases. The rest of the 54 blocks have higher than one relative risk due to the higher observed than expected cases. In comparison, three blocks reported more than ten relative risks, including Bhagawangola-II (RR\u0026thinsp;=\u0026thinsp;11.11) from cluster 3, Binpur-II (RR\u0026thinsp;=\u0026thinsp;22.47) and Ranibadh (RR\u0026thinsp;=\u0026thinsp;31.9) from cluster 1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpace Time Cluster of P. vivax\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime Period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObserved Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExpected Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo. of Blocks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelative Risk (RR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApril11- Nov14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e129.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJun17-Dec17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApril12- Nov16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2945.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAug17-Oct17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e276.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeb18-Nov18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJun11-Aug13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJul17-Oct17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e237.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Univariate Space Time Cluster of P. falciparum\u003c/h2\u003e \u003cp\u003eThe univariate space-time cluster analysis of monthly P. falciparum malaria cases identified a non-random distribution of cases at the Block level in West Bengal from April 2011 to March 2021. The analysis detected 18 significant clusters affecting 69 blocks for P. falciparum cases (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The most observed cases (n\u0026thinsp;=\u0026thinsp;8224) found in cluster 1, centered at Barabazar block from October 2011 to September 2016, have the most extended duration cluster. Out of 18 clusters, 10 clusters form a self-centered cluster due to higher observed cases. Eight clusters were detected from 2011 to 2016, centered at Matiali (cluster 2 from July 2011 to December 2011), Kaliachak-I (cluster 3 from August 2012 to November 2012), Illambazar (cluster 4 from June 2013 to July 2013), Khoyrasol (cluster 6 during July 2011 to September 2011), Murshidabad Jiaganj (cluster 7 during July 2012 to November 2012), Potashpur- I ( cluster 9 during June 2011 to November 2012), Kolaghat (cluster 11 during October 2013 to February 2016) and Tahatta (cluster 14 during November 2014 to March 2015). Two thousand seventeen two clusters were reported, centered at Manteswar (cluster 12 from April 2017 to May 2017) and Mandirbazar (cluster 16 from August 2017 to November 2017). From 2018 to 2019, two clusters were detected, centered at Hanskhali (cluster 13 from July 2018 to February 2019) and Shyampur (cluster 17 from September 2018 to Oct 2018). From 2020 onwards, five clusters were identified with a duration between one to three months, out of which three clusters centered at Jangipara (cluster 5 from January 2020 to March\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpace Time Cluster of P. falciparum\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime Period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObserved Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExpected Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo. of Blocks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelative Risk (RR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOct11 - Sep16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e603.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJul11 - Dec11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e116.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAug12 - Nov12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJun13 - Jul13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e118.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJan20 - Mar20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJul11 - Sep11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJul12 - Nov12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeb21 - Feb21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJun11 - Nov12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJan21 - Mar21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOct13 - Feb16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApril17 - May17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJul18 - Feb19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNov14 - May15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJan21 - Mar21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAug17 - Nov17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSep18 - Oct18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOct20 - Nov20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e2020), Haringhata (cluster 8 from February 2021), Baruipur (cluster 18 from October 2020 to November 2020). Another two clusters centered at Ranaghat- I (cluster 10) and Moyna (cluster 15) from January 2021 to March 2021. While the highest observed cases were reported from cluster 1, cluster 4 was identified with the highest relative risk due to the lowest number of expected P. falciparum cases.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(A) represents the space-time cluster of P. falciparum cases from April 2011 to March 2021. Right side Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(B) depicts each block's statistically significant relative risk of P. falciparum from the space-time clusters. Out of 341 blocks, 25 blocks assigned zero relative risk due to no observed P. falciparum cases reported. Of 69 blocks assigned to a cluster, 28 blocks reported relative risk\u0026thinsp;\u0026lt;\u0026thinsp;1 due to lower observed than expected cases. The other 41 blocks have higher than one relative risk due to the higher observed than expected cases. From cluster 1, out of 31 blocks eight blocks reported relative risk\u0026thinsp;\u0026gt;\u0026thinsp;10 including Ranibadh (RR\u0026thinsp;=\u0026thinsp;10.89), Jhalda- II (RR\u0026thinsp;=\u0026thinsp;14.04), Arsha (RR\u0026thinsp;=\u0026thinsp;21.35), Binpur-II (RR\u0026thinsp;=\u0026thinsp;27.76), Jhalda- I (RR\u0026thinsp;=\u0026thinsp;28.97), Balaramapur (RR\u0026thinsp;=\u0026thinsp;30.66), Bagmundi (63.35) and Banduan (RR\u0026thinsp;=\u0026thinsp;80.02).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Multivariate Space Time Cluster\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the result of the multivariate space-time clusters analysis, where nine clusters detected the coexistence of P. vivax and P. falciparum, affecting 133 blocks. Clusters 1 to 3 and 5 were formed with P. vivax and P. falciparum, while clusters 4 and 9 were formed with only P. vivax and clusters 6\u0026ndash;8 were formed with P. falciparum. Four clusters detected with P. vivax and P. falciparum centered at Balarampur (cluster 1 from April 2011 to March 2016 with 16276 observed cases), Farakka (cluster 2 from April 2012 to November 2016 with 10539 observed cases), Kalchini (cluster 3 from June 2017 to December 2017) and Jaypur (cluster 5 during February 2018 to November 2018). While Cluster 4, centered at Magrahat- II (from August 2017 to October 2017) and Cluster 9, centered at Pandua (from June 2011 to August 2013), formed with P. vivax cases only, though P. falciparum cases were observed there, the analysis could not find any statistically significant space-time cluster. An alternative scenario took place with three clusters, including Cluster 6, centered at Illambazar (from June 2013 to July 2013); Cluster 7, centered at Jangipara (from January 2020 to March 2020); and Cluster 8, centered at Khoyrasol (from July 2011 to September 2011) with P. falciparum cases only. There was no evidence to construct a significant space-time cluster in those cases.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Space Time Cluster\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime Period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVectors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObserved Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExpected Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelative Risk (RR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo. of Blocks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eApr11 - Mar16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1439.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e482.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eApr12 - Nov16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2945.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1053.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eJun17 - Dec17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAug17- Oct17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e276.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFeb18 - Nov18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eJun13 - Jul13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e118.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eJan20 - Mar20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eJul11 - Sep11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eJun11 - Aug13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn comparison with both results, there is the presence of the same clusters in a multivariate analysis that was also detected in univariate space-time P. vivax and P. falciparum analysis. Multivariate clusters 2, 3, and 4 are the same group of clusters that contain the same blocks in univariate P. vivax clusters 3, 2 and 4, whereas multivariate cluster 1 contains a portion of P. falciparum cluster 1. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA represents the multivariate cluster, which represents the co-occurrence of P. vivax and P. falciparum cases from April 2011 to March 2021. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(B) depicts the statistically significant relative risk of multivariate clusters of each block from the space-time cluster analysis. In multivariate analysis, 133 blocks are combinedly affected by the co-occurrence of P. vivax and P. falciparum. Out of 133 blocks, 68 reported\u0026thinsp;\u0026lt;\u0026thinsp;1 relative risk, and the rest of the 65 blocks detected as \u0026gt;\u0026thinsp;1 relative risk. After all, multivariate space-time scan statistics helped to identify the co-occurrence of both P. vivax and P. falciparum. In contrast, the multivariate study highlights the regions significantly affected by both malaria vectors. Further studies are required to understand the complex spatial variation of vectors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eUsing ten years of retrospective surveillance data, this study demonstrated seasonal variability and significant space-time clusters of P. vivax and P. falciparum malaria vectors at the block level in West Bengal during April 2011 to March 2021. Throughout the study period, seasonal occurrence of malaria vectors maximized during monsoon and post-monsoon, which follow a seasonal oscillating pattern, signify a strong relation with monsoon climate. During monsoon, favourable environmental conditions accelerate the breeding and growth of malaria vectors as well as transmission. Even malaria vectors can remain dormant for a long time until they get favourable conditions. Therefore, focused interventions are required, including monitoring and distribution of LLINs, ITN, vector control strategies with chemical and biological, community participation, environmental management, garbage cleaning, and awareness campaign should be strengthened to interrupt local transmission (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith the help of retrospective univariate and multivariate space-time scan statistics, both analyses detected significant clusters of P. vivax and P. falciparum individually and co-occurring of both vectors. From the analysis, non-random distribution of malaria vectors has been found to create different sizes of clusters of both vectors through the study area in different periods. High-risk spatiotemporal clusters of P. vivax were detected across cross border area in 34 blocks from Maldah, Murshidabad and Birbhum districts located at the central part of West Bengal along with Bangladesh and Jharkhand border. Another high-risk spatiotemporal cluster of P. vivax is found in northern part along with Bhutan and Assam border. Although, these areas are considering as porous border with Bangladesh which act as cross border illegal migration (Fambirai et al., 2022; Gupta et al., 2022). Therefore, cross border population movement and sharing a long international border are likely to be a constant danger of influx of malaria cases from high-endemic to low-endemic areas (Penjor et al., 2023; WHO, 2019).\u003c/p\u003e \u003cp\u003eHigh risk spatiotemporal clusters of P. falciparum were observed in the western part of West Bengal with Puruliya, Bankura, Jhargram districts covered by forest area. This cluster also identified as the most prolonged time high-risk zone before 2017 and persisted over five years, contributing more than 85% P. falciparum cases. Dense forests, hills, perennial streams and high tribal and marginalised population groups living in improvised conditions (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) potentially contribute to this situation. The risk of malaria transmission across these regions are quite high as local population living in the forest fringe and tribal areas which is physio-graphically mosquito-prone area. However, prevalence of malaria cases decreased in Puruliya district from 2017 onwards which is possible with proper intervention and distribution of LLINs on time (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study also identified expansion in the geographical distribution of P. falciparum over P. vivax in the southern part of the Gangetic plain, especially after 2017. The emergence of P. Falciparum clusters in the newer regions requires targeted interventions to disrupt further transmission. As the elimination process is progressing to be malaria-free by 2027 and to eliminate the disease by 2030, the road ahead is bumpy and beset with many challenges, including the emergence of a set of new asymptomatic or sub-microscopic malaria parasitaemia (\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), the emergence of Artemisinin and multidrug-resistant malaria (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Likewise, the elimination of malaria fever incidence and measuring transmissivity requires a strong surveillance system, which helps to detect infection early and enables a rapid and effective response.\u003c/p\u003e \u003cp\u003eFrom the analysis distinct patterns were seen in the spatial variations between P. vivax and P. falciparum malaria, indicating that the biological features of the parasites varied and might have played a role in their transmission. The activity of vectors and the length of parasite incubation are influenced by climate, and these factors are associated with a higher risk of malaria (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). P. falciparum needs a slightly higher temperature for parasite growth than P. vivax. As for both vectors, the minimal threshold temperature is around 18 and 15\u0026deg;C, respectively; there is a greater chance that P. falciparum may spread to formerly colder locations due to global climate change (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Further investigation is required to fully understand the risk factors driving the geographic distribution and spatial shifting of P. falciparum throughout West Bengal.\u003c/p\u003e \u003cp\u003eFurthermore, the univariate study independently examines both vectors' spatial and temporal variation and highlights the burden of P. vivax and P. falciparum. In contrast, the multivariate study highlights the regions significantly affected by both malaria vectors. However, the co-occurrence of both vectors in four high-risk clusters signifies the transmission dynamics, though no clinical or microscopic coinfection has been recorded. Four multivariate clusters with P. vivax and P. falciparum were observed from April 2011 to November 2018. The coexistence of both vectors in the same geographical region within the same climatic condition indicates that seasonal factors may influence the co-occurrence, giving a call for vector control monitoring. Given the frequent coexistence of P. vivax and P. falciparum in India, prioritizing intervention actions becomes challenging due to potential variations in disease transmission between these species (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). As the objective of this is to identify the existence of space-time clusters and seasonal variability of malaria vectors, consequent studies are required to identify determining factors associated with this co-occurrence.\u003c/p\u003e \u003cp\u003eHowever, this study has a variety of limitations. First, for this study block level administrative unit has been taken but small scale like village level unit will help better understanding for disease outbreak and surveillance. Second important limitation is relative risk reported by each cluster. Reported relative risk for each cluster was detected underlying total population, which does not consider sociodemographic variation. In areas of low transmission all age group are vulnerable but adults develop more severe and multiple complications. In areas of high transmission children below 5 years, visitors and migratory labour are higher at risk. Pregnant women are less capable of coping with and clearing malaria infection, adversely affecting the unborn fetus. Therefore, adjusted rate based on age, sex and other socio-economic parameters might generate accurate relative risk. Third, though SaTScan is an important statistical tool for spatial and space time cluster analysis, challenging to determine the maximum window limit for spatial and temporal extension, as a result misdiagnosis of small and heterogenous clusters. Last, block level population data was taken from last census occurred in 2011. Using outdated demographic data is a critical issue effecting the findings of the research and a common limitation in many studies in developing countries. Despite having various limitations, this study is a first attempt to examine the seasonal variability and spatio-temporal cluster of malaria vectors and its cooccurrence at the block level using 120 months longitudinal dataset. Identifying the covariate effects and transmission in the spatiotemporal context, ecological modeling like autoregressive study is required which will avenue the future target.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eFrom the purview of spatiotemporal extent, the space-time cluster analysis shows a robust spatiotemporal relationship between malaria vector prevalence in West Bengal. This study found significant univariate and multivariate space-time clusters of P. vivax and P. falciparum malaria vectors, as well as their co-occurrence and seasonal fluctuation. The detected clusters and the relative risk of each cluster were also visualized to improve the understanding of the malaria epidemic throughout the study area. As NVBDCP is approaching the elimination procedure by targeting to be malaria-free by 2027 and to eliminate the disease by 2030, disease prevention and control lending community support are much needed along with following up cross-border migration, supplying LLINs and IRS, and the advent of ACTs and application of more sensitive tools like polymerase chain reaction (PCR) and Loop-mediated isothermal amplification (LAMP) with conventional methods to overcome the challenge of malaria elimination. Addressing the determinants of malaria transmission in these varied clusters necessitates regional collaboration and strategic plans, which are critical steps towards overcoming the remaining hurdles in malaria eradication.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVBDs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVector Borne Diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGTS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobal Technical Strategy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPLMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAsia Pacific Leaders Malaria Alliance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNFME\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Framework for Malaria Elimination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEPDT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEarly case Detection and Prompt Treatment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNVBDCP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Vector Borne Disease Control Programme\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eITN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInsecticide-treated bed nets\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLLIN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLong-Lasting Insecticidal Nets\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArtemisinin based Combination Therapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIndoor Residual Sprays\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntegrated Vector Management\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSpace Time Scan Statistics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRelative Risk\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase Chain Reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLAMP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLoop-mediated isothermal amplification\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analysed in this study was taken from the Ministry of Health and Family Welfare, Government of India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere has been no significant financial support for this work that could have influenced its outcome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMM developed methodology, performed analysis, interpreted the outcome and prepared the original draft. UR supervised the investigation and reviewed the final draft. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. Vector-borne diseases [Internet]. 2020 [cited 2023 Jul 27]. Available from: https://www.who.int/news-room/fact-sheets/detail/vector-borne-diseases\u003c/li\u003e\n\u003cli\u003eWHO. World malaria report 2022 [Internet]. Geneva; 2022 [cited 2023 Jul 26]. Available from: https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2022\u003c/li\u003e\n\u003cli\u003eNVBDCP G, DGHS, MFHW. National Framework forMalaria Elimination in India (2016-2030) [Internet]. India; 2016. Available from: https://nvbdcp.gov.in/WriteReadData/l892s/National-framework-for-malaria-elimination-in-India-2016%E2%80%932030.pdf\u003c/li\u003e\n\u003cli\u003eNVBDCP. National Vector Borne Disease Control Programme [Internet]. 2021 [cited 2019 May 7]. Available from: https://www.nvbdcp.gov.in/\u003c/li\u003e\n\u003cli\u003eWangdi K, Penjor K, Tobgyal, Lawpoolsri S, Price RN, Gething PW, et al. Space\u0026ndash;Time Clustering Characteristics of Malaria in Bhutan at the End Stages of Elimination. IJERPH. 2021 May 22;18(11):5553. \u003c/li\u003e\n\u003cli\u003eHFWD. State Vector Borne Diseases Control and Seasonal Influenza Plan, 2018 [Internet]. Health \u0026amp; Family Welfare Department, Government of West Bengal; 2018. Available from: https://www.wbhealth.gov.in/uploaded_files/ticker/State_Vector_Borne_2018.pdf\u003c/li\u003e\n\u003cli\u003eHay SI, Battle KE, Pigott DM, Smith DL, Moyes CL, Bhatt S, et al. Global mapping of infectious disease. Philosophical Transactions of the Royal Society B: Biological Sciences. 2013 Feb 4;368(1614):20120250\u0026ndash;20120250. \u003c/li\u003e\n\u003cli\u003eKulldorff M, Nagarwalla N. Spatial disease clusters: detection and inference. Stat Med. 1995 Apr 30;14(8):799\u0026ndash;810. \u003c/li\u003e\n\u003cli\u003eClements ACA, Reid HL, Kelly GC, Hay SI. Further shrinking the malaria map: how can geospatial science help to achieve malaria elimination? Lancet Infect Dis. 2013 Aug;13(8):709\u0026ndash;18. \u003c/li\u003e\n\u003cli\u003eColeman M, Coleman M, Mabuza AM, Kok G, Coetzee M, Durrheim DN. Using the SaTScan method to detect local malaria clusters for guiding malaria control programmes. Malaria Journal. 2009 Apr 17;8(1):68. \u003c/li\u003e\n\u003cli\u003eWangdi K, Pasaribu AP, Clements ACA. Addressing hard-to-reach populations for achieving malaria elimination in the Asia Pacific Malaria Elimination Network countries. Asia \u0026amp; the Pacific Policy Studies. 2021;8(2):176\u0026ndash;88. \u003c/li\u003e\n\u003cli\u003eIkeda T, Behera SK, Morioka Y, Minakawa N, Hashizume M, Tsuzuki A, et al. Seasonally lagged effects of climatic factors on malaria incidence in South Africa. Sci Rep. 2017 May 29;7(1):2458. \u003c/li\u003e\n\u003cli\u003eNguyen M, Howes RE, Lucas TCD, Battle KE, Cameron E, Gibson HS, et al. Mapping malaria seasonality in Madagascar using health facility data. BMC Medicine. 2020 Feb 10;18(1):26. \u003c/li\u003e\n\u003cli\u003eStuckey EM, Smith T, Chitnis N. Seasonally Dependent Relationships between Indicators of Malaria Transmission and Disease Provided by Mathematical Model Simulations. PLOS Computational Biology. 2014 Sep 4;10(9):e1003812. \u003c/li\u003e\n\u003cli\u003eKulldorff M. A spatial scan statistic. Communications in Statistics - Theory and Methods. 1997 Jan;26(6):1481\u0026ndash;96. \u003c/li\u003e\n\u003cli\u003eKulldorff M. Prospective Time Periodic Geographical Disease Surveillance Using a Scan Statistic. Journal of the Royal Statistical Society Series A: Statistics in Society. 2001 Jan 1;164(1):61\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eKulldorff M, Mostashari F, Duczmal L, Katherine Yih W, Kleinman K, Platt R. Multivariate scan statistics for disease surveillance. Stat Med. 2007 Apr 15;26(8):1824\u0026ndash;33. \u003c/li\u003e\n\u003cli\u003eBrooker S, Clarke S, Njagi JK, Polack S, Mugo B, Estambale B, et al. Spatial clustering of malaria and associated risk factors during an epidemic in a highland area of western Kenya. Trop Med Int Health. 2004 Jul;9(7):757\u0026ndash;66. \u003c/li\u003e\n\u003cli\u003eHundessa SH, Williams G, Li S, Guo J, Chen L, Zhang W, et al. Spatial and space\u0026ndash;time distribution of Plasmodium vivax and Plasmodium falciparum malaria in China, 2005\u0026ndash;2014. Malaria Journal. 2016 Dec 19;15(1):595. \u003c/li\u003e\n\u003cli\u003eDesjardins MR, Whiteman A, Casas I, Delmelle E. Space-time clusters and co-occurrence of chikungunya and dengue fever in Colombia from 2015 to 2016. Acta Tropica. 2018 Sep;185:77\u0026ndash;85. \u003c/li\u003e\n\u003cli\u003eXu F, Beard K. A comparison of prospective space-time scan statistics and spatiotemporal event sequence based clustering for COVID-19 surveillance. PLOS ONE. 2021 Jun 10;16(6):e0252990. \u003c/li\u003e\n\u003cli\u003eLi J, Kolivras KN, Hong Y, Duan Y, Seukep SE, Prisley SP, et al. Spatial and temporal emergence pattern of Lyme disease in Virginia. Am J Trop Med Hyg. 2014 Dec;91(6):1166\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eNsoesie EO, Ricketts RP, Brown HE, Fish D, Durham DP, Mbah MLN, et al. Spatial and Temporal Clustering of Chikungunya Virus Transmission in Dominica. PLOS Neglected Tropical Diseases. 2015 Aug 14;9(8):e0003977. \u003c/li\u003e\n\u003cli\u003eNakaya T, Yano K. Visualising Crime Clusters in a Space-time Cube: An Exploratory Data-analysis Approach Using Space-time Kernel Density Estimation and Scan Statistics. Transactions in GIS. 2010;14(3):223\u0026ndash;39. \u003c/li\u003e\n\u003cli\u003eSteelesmith DL, Lindstrom MR, Le HTK, Root ED, Campo JV, Fontanella CA. Spatiotemporal Patterns of Deaths of Despair Across the U.S., 2000-2019. Am J Prev Med. 2023 Aug;65(2):192\u0026ndash;200. \u003c/li\u003e\n\u003cli\u003eParizo J, Sturrock HJW, Dhiman RC, Greenhouse B. Spatiotemporal Analysis of Malaria in Urban Ahmedabad (Gujarat), India: Identification of Hot Spots and Risk Factors for Targeted Intervention. Am J Trop Med Hyg. 2016 Sep 7;95(3):595\u0026ndash;603. \u003c/li\u003e\n\u003cli\u003eShekhar S, Yoo EH, Ahmed SA, Haining R, Kadannolly S. Analysing malaria incidence at the small area level for developing a spatial decision support system: A case study in Kalaburagi, Karnataka, India. Spatial and Spatio-temporal Epidemiology. 2017 Feb 1;20:9\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eCleveland RB, Cleveland WS, McRae JE, Terpenning I. STL: A Seasonal-Trend Decomposition Procedure Based on Loess. Journal of Official Statistics. 1990;6(1):3\u0026ndash;73. \u003c/li\u003e\n\u003cli\u003eFambirai T, Chimbari MJ, Ndarukwa P. Global Cross-Border Malaria Control Collaborative Initiatives: A Scoping Review. Int J Environ Res Public Health. 2022 Sep 26;19(19):12216. \u003c/li\u003e\n\u003cli\u003eGupta SK, Saroha P, Singh K, Saxena R, Barman K, Kumar A, et al. Malaria Epidemiology Along the Indian Districts Bordering Bhutan and Implications for Malaria Elimination in the Region. Am J Trop Med Hyg. 2022 Feb;106(2):655\u0026ndash;60. \u003c/li\u003e\n\u003cli\u003ePenjor K, Zangpo U, Tshering D, Ley B, Price RN, Wangdi K. Imported malaria and its implication to achievement of malaria-free Bhutan. Journal of Travel Medicine. 2023 Apr 1;30(3):taad044. \u003c/li\u003e\n\u003cli\u003eWHO. Meeting on cross border collaboration on malaria elimination along the India- Bhutan border [Internet]. Guwahati, Assam, India: World Health Organization, South-East Asia; 2019. Available from: https://www.who.int/docs/default-source/searo/malaria/cross-border-malaria-meeting-report.pdf?sfvrsn=85dbe22c_2\u003c/li\u003e\n\u003cli\u003eAnvikar A, Dev V. Malaria transmission in India: disease distribution and prevalence of mosquito vectors in different physiographic zones. In: Vector Biology and Control: An Update for Malaria Elimination Initiative in India [Internet]. New Delhi, India: The National Academy of Sciences (NASI); 2020. p. 117\u0026ndash;28. Available from: https://nimr.org.in/images/pdf/BCIL_vector.pdf\u003c/li\u003e\n\u003cli\u003ePradhan S, Hore S, Maji SK, Manna S, Maity A, Kundu PK, et al. Study of epidemiological behaviour of malaria and its control in the Purulia district of West Bengal, India (2016-2020). Sci Rep. 2022 Jan 12;12(1):630. \u003c/li\u003e\n\u003cli\u003eDeora N, Yadav CP, Pande V, Sinha A. A systematic review and meta-analysis on sub-microscopic Plasmodium infections in India: Different perspectives and global challenges. The Lancet Regional Health - Southeast Asia [Internet]. 2022 Jul 1 [cited 2023 Aug 31];2. Available from: https://www.thelancet.com/journals/lansea/article/PIIS2772-3682(22)00012-9/fulltext#%20\u003c/li\u003e\n\u003cli\u003eKaura T, Kaur J, Sharma A, Dhiman A, Pangotra M, Upadhyay AK, et al. Prevalence of submicroscopic malaria in low transmission state of Punjab: A potential threat to malaria elimination. J Vector Borne Dis. 2019;56(1):78\u0026ndash;84. \u003c/li\u003e\n\u003cli\u003eKumari P, Sinha S, Gahtori R, Yadav CP, Pradhan MM, Rahi M, et al. Prevalence of Asymptomatic Malaria Parasitemia in Odisha, India: A Challenge to Malaria Elimination. The American Journal of Tropical Medicine and Hygiene. 2020 Oct 7;103(4):1510\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003evan Eijk AM, Sutton PL, Ramanathapuram L, Sullivan SA, Kanagaraj D, Priya GSL, et al. The burden of submicroscopic and asymptomatic malaria in India revealed from epidemiology studies at three varied transmission sites in India. Sci Rep. 2019 Nov 19;9:17095. \u003c/li\u003e\n\u003cli\u003eChoubey D, Deshmukh B, Rao AG, Kanyal A, Hati AK, Roy S, et al. Genomic analysis of Indian isolates of Plasmodium falciparum: Implications for drug resistance and virulence factors. International Journal for Parasitology: Drugs and Drug Resistance. 2023 Aug 1;22:52\u0026ndash;60. \u003c/li\u003e\n\u003cli\u003eDhorda M, Amaratunga C, Dondorp AM. Artemisinin and multidrug-resistant Plasmodium falciparum \u0026ndash; a threat for malaria control and elimination. Curr Opin Infect Dis. 2021 Oct;34(5):432\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eWang D, Li S, Cheng Z, Xiao N, Cotter C, Hwang J, et al. Transmission Risk from Imported Plasmodium vivax Malaria in the China\u0026ndash;Myanmar Border Region. Emerg Infect Dis. 2015 Oct;21(10):1861\u0026ndash;4. \u003c/li\u003e\n\u003cli\u003ePatz JA, Olson SH. Malaria risk and temperature: Influences from global climate change and local land use practices. Proc Natl Acad Sci U S A. 2006 Apr 11;103(15):5635\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eDhiman RC, Chavan L, Pant M, Pahwa S. National and regional impacts of climate change on malaria by 2030. Current Science. 2011;101(3):372\u0026ndash;83. \u003c/li\u003e\n\u003cli\u003eBhattacharya S, Sharma C, Dhiman R, Mitra A. Climate change and malaria in India. Curr Sci India. 2005 Nov 30;90. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Kolkata, the urban metropolitan city and also district in West Bengal, is also a malaria burden zone with API\u0026thinsp;\u0026gt;\u0026thinsp;1. For this study, Kolkata has been excluded for spatio-temporal analysis due to unavailability of case data during the time frame.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Vector borne diseases, malaria, STL, space-time scan statistics, co-occurrence.","lastPublishedDoi":"10.21203/rs.3.rs-3888752/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3888752/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMalaria, a prominent Vector Borne Diseases (VBDs) causing over a million annual deaths worldwide, predominantly affects vulnerable populations in the least developed regions. Despite their preventable and treatable nature, malaria remains a global public health concern. In the last decade, India has faced a significant decline in malaria morbidity and mortality. As India pledged to eliminate malaria by 2030, this study examined a decade of surveillance data to uncover space-time clustering and seasonal trends of Plasmodium vivax and falciparum malaria vectors in West Bengal.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSeasonal and Trend decomposition using Loess (STL) was applied to detect seasonal trend and anomaly of the time series. Univariate and multivariate space-time cluster analysis of both vectors was performed at block level using Kulldorff's space-time scan statistics from April 2011 to March 2021 to detect statistically significant space-time clusters.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFrom the time series decomposition, a clear seasonal pattern is visible for both vectors. Statistical analysis indicated considerable high-risk P. vivax clusters, particularly in the northern, central, and lower Gangetic areas. Whereas, P. falciparum was concentrated in the western region with a significant recent transmission towards the lower Gangetic plan. From the multivariate space-time scan statistics, the co-occurrence of both vectors was detected with four significant clusters, which signifies the regions experiencing a greater burden of malaria vectors.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis non-random distribution underscores the urgency for dynamic monitoring and targeted interventions. Significant geographical and spatiotemporal heterogeneity was evident for both malaria vectors, emphasizing the need for tailored approaches. Identifying co-occurring clusters offers crucial insights into disease risk, paving the way for focused control initiatives. Addressing the drivers of malaria transmission in these diverse clusters demands regional cooperation and strategic strategies, crucial steps towards overcoming the final obstacles in malaria eradication.\u003c/p\u003e","manuscriptTitle":"Evaluating Space Time Cluster and Co-occurrence of Malaria Vectors of West Bengal in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 16:21:46","doi":"10.21203/rs.3.rs-3888752/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-17T21:31:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-08T09:41:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136edce2-4554-4aba-9675-7a49aff0ca61","date":"2024-01-30T02:55:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43af6901-eba1-47e2-8a39-ca0f171c3a13","date":"2024-01-29T18:47:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"e4b5112f-8de6-4cc6-b431-116e14bbabf6","date":"2024-01-29T04:28:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-24T18:04:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-24T15:09:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-24T04:25:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Malaria Journal","date":"2024-01-22T18:41:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e67fc70c-dc54-440a-a073-212a6d6d1bef","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-10T06:09:01+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-25 16:21:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3888752","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3888752","identity":"rs-3888752","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}