J Arthropod-Borne Dis, March 2021, 15(1): 108–125 A Ghanbarnejad et al.: Spatial Modelling of … 108 http://jad.tums.ac.ir Published Online: March 31, 2021 Original Article Spatial Modelling of Malaria in South of Iran in Line with the Implementation of the Malaria Elimination Program: A Bayesian Poisson-Gamma Random Field Model Amin Ghanbarnejad1; Habibollah Turki2; Mehdi Yaseri1; Ahmad Raeisi3,4; *Abbas Rahimi- Foroushani1 1Department of Epidemiology and biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran 2Infectious and Tropical Diseases Research Center, Hormozgan Health Institute, Hormozgan University of Medical Sciences, Bandar Abbas, Iran 3Departments of Medical Parasitology and Mycology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran 4Center for Communicable Diseases Control, Ministry of Health and Medical Education, Tehran, Iran *Corresponding author: Dr Abbas Rahimi-Foroushani, E-mail: rahimifo@tums.ac.ir (Received 13 Aug 2020; accepted 30 Mar 2021) Abstract Background: Malaria is the third most important infectious disease in the world. WHO propose programs for control- ling and elimination of the disease. Malaria elimination program has begun in first phase in Iran from 2010. Climate factors play an important role in transmission and occurrence of malaria infection. The main goal is to investigate the spatial distribution of incidence of malaria during April 2011 to March 2018 in Hormozgan Province and its association with climate covariates. Methods: The data included 882 confirmed cases gathered from CDC in Hormozgan University of Medical Sciences. A Poisson-Gamma Random field model with Bayesian approach was used for modeling the data and produces the smoothed standardized incidence rate (SIR). Results: The SIR for malaria ranged from 0 (Abu Musa and Haji Abad districts) to 280.57 (Bandar–e-Jask). Based on model, temperature (RR= 2.29; 95% credible interval: (1.92–2.78)) and humidity (RR= 1.04; 95% credible interval: (1.03–1.06)) had positive effect on malaria incidence, but rainfall (RR= 0.92; 95% credible interval: (0.90–0.95)) had negative impact. Also, smoothed map represent hot spots in the east of the province and in Qeshm Island. Conclusion: Based on the analysis of the study results, it was found that the ecological conditions of the region (tem- perature, humidity and rainfall) and population displacement play an important role in the incidence of malaria. There- fore, the malaria surveillance system should continue to be active in the region, focusing on high-risk areas of malaria. Keywords: Bayesian; Spatial; Poisson-Gamma; Hormozgan; Malaria elimination Introduction Malaria is the third most important infec- tious disease after tuberculosis and AIDS and one of the tenth diseases under investigation by the WHO as part of a program, called Tropi- cal Disease Research. Malaria threatens more than 40% of the world's population. It is esti- mated that about 2.5 billion people worldwide are at risk of malaria (1). According to the re- port by WHO in 2019, about 80 countries are facing with malaria transmission which had led to 228 million infected cases and 405,000 death (1). The disease is present in most tropi- cal, subtropical and even temperate regions of the world. Due to the nature of the disease and the complexity of malaria epidemiology, it is not possible to solve the disease problem with a single strategy. Malaria is not only an infec- tious disease of the tropical areas, but also a Copyright © 2020 The Authors. Published by Tehran University of Medical Sciences. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International license (https://creativecommons.org/licenses/by- nc/4.0/). Non-commercial uses of the work are permitted, provided the original work is properly cited. http://jad.tums.ac.ir/ https://creativecommons.org/licenses/by-nc/4.0/ https://creativecommons.org/licenses/by-nc/4.0/ J Arthropod-Borne Dis, March 2021, 15(1): 108–125 A Ghanbarnejad et al.: Spatial Modelling of … 109 http://jad.tums.ac.ir Published Online: March 31, 2021 health problem related to climatic, social, eco- nomic and developmental conditions in the world. Iran is located in an endemic area of ma- laria according to the malaria's global distri- bution map (2). Although ongoing efforts to combat malaria in the last five decades have led to a significant reduction in malaria in Iran, there are still positive cases of malaria, espe- cially in the south and south-east of the coun- try. The incidence of malaria in Iran has de- creased from 15378 cases in 2002 to 960 cas- es in 2017 (3). The three provinces including Kerman, Sistan and Baluchestan, and Hor- mozgan are the most malaria-prone areas of Iran (4, 5). This disease has been prevalent as hyper-endemic in many parts of Hormozgan Province for many years. 12% of malaria cas- es reported in the south-eastern region and 10% of cases reported in Iran belong to Hor- mozgan Province (6). WHO had an elimination program for some countries and first phase of implementation in Iran was during 2010 to 2015 and second phase was begun in 2016 and run successfully in the region (7-10). Appropriate weather and humidity conditions throughout the year for carrier activity, adjacent to Sistan and Baluchistan Province as the main malarious ar- ea of Iran, as well as relocation and presence of immigrants infected with malaria, especial- ly Pakistani and Afghani, are the most important factors affecting the malaria situation in this province (6). Many studies around the world have linked the incidence of malaria to weath- er conditions (11-19). Therefore, the use of cli- mate variables will help to improve the pre- diction of malaria cases. Malaria modeling in the context of spatial models and mapping is one of the most useful approaches to study ma- laria in relation to climate factors. For the map- ping of diseases, including malaria, the data ap- plied are mainly the number of occurrences in each area of the country, namely province or county. One set of statistical models that is wide- ly used in disease mapping is Bayesian hierar- chical spatial models. The most commonly used models in this domain are Markov random field models proposed for the first time by Besag et al. in 1991 (20) and the ecological regression model developed by Clayton and Bernard- inelli in 1992 (21) . In the aforementioned models, spatial qual- ity and resolution of the risk surface function is similar to the resolution at which the data is measured. To overcome this limitation, Wolpert and Ickstadt (1998) (22), presented an exten- sion of the random field to the hierarchical mod- el introduced by Clayton and Kaldor (1987) (23), in which the spatial resolution of risk sur- face function could be independent of where the data measured. This model was applied in epidemiology by Best et al. (2005) (24) and co- variates were used to improve the risk surface function in the model. In this study, the rela- tionship between malaria incidence and cli- mate factors will be assessed based on extend- ed Poisson-Gamma random field model and produce the malaria risk map in Hormozgan Province at county level. The main goal of this study was to design and develop a model to investigate and predict the conditions of ma- laria transmission in the endemic region of Hormozgan in accordance with successful im- plementation of the malaria elimination pro- gram. Materials and Methods Study area Iran has diverse climate and is in the en- demic area of the global malaria spread map (2). Most of the positive cases are in the south and south-east of the country, where malaria elimination program is in progress. The three provinces of Kerman, Sistan and Baluchestan and Hormozgan are malaria-prone areas of Iran. Hormozgan Province is one of the malaria-prone areas of the country and has been hyper-en- demic for many years (25). Hormozgan Province is located in south of Iran, has about 1,000km of coastline. Its population was 1,578,183 based http://jad.tums.ac.ir/ J Arthropod-Borne Dis, March 2021, 15(1): 108–125 A Ghanbarnejad et al.: Spatial Modelling of … 110 http://jad.tums.ac.ir Published Online: March 31, 2021 on 2016 census data. Its area is 70,697km2 and its provincial capital is Bandar Abbas. Data collection Data on malaria incidence during April 2011 to March 2018 in Hormozgan Province was used for analysis. These data are collected from communicable disease center in Health Dep- uty of Hormozgan University of medical sci- ences. The data were grouped based on loca- tion of occurrence and categorized at county level. The data on climate variables gathered from Hormozgan meteorological organization (HMO). The variables temperature, humidity and rainfall were available as monthly record. For analysis, we calculated mean of the varia- bles in each year, then averaging on 6 sur- veyed years and considered it as mentioned covariate for each district in the model. Statistical Model For response variable, we analyzed observed numbers of malaria cases (i: county (1, …, 14)). The model used in this paper was a Pois- son-gamma random field model with rainfall, temperature and humidity as covariates, the fol- lowing Bayesian hierarchical structure was con- sidered for modeling framework: Level 1: Points: Intensity: Level 2: Latent sources: Level 3: Parameter: In the above context, the number of ob- served malaria cases in each region is mod- elled by a Poisson process on Y with mean , where is the popu- lation reference measure. as mean of the Poisson process depends on a set of covariates which their effect can be considered as excess risk (JA) or relative risk (JM). The spatial effect is introduced with a latent covariate which is modelled as a Gaussian kernel mixture of a random measure on space S. We choose S as bounding box of area of Hormozgan Prov- ince. In this paper, a Gamma random field with shape measure α(ds) and inverse scale function was used. in- cluded latent sources s, , located at with size . Bivariate Gaussian kernel with correlated longitude and latitude is as follow: We consider four latent sources at fixed lo- cation of each kernel for model- ling. The SIR (Standardized Incidence Ratio) was calculated as below: is the expected number of malaria cases in region i which calculated as, , is the total rate of malaria in the study region. Results Descriptive statistics The study included 882 registered cases in period from April 2011 to March 2018. Based on epidemiological classifications, 716 cases (81.2%) were imported. Demographic charac- teristics of the cases are presented in Table 1. About half of the registered cases were locat- ed in rural areas. Majority of cases were non- Iranian (81.5%). Mean age of the patients was 24.3±13.68 and ranged from infants to 96 years old person. Men are more infected than wom- en (84.96 versus 13.44 per 100,000 populations). The data was aggregated and grouped in ad- ministrative district, county for model fitting. The incidence rate of malaria per 100,000 per- sons was calculated as number of case in each region divided by population multiplied by 100,000 and showed in Fig. 1. The Incidence rate ranged from 0 (Abu Musa and Haji Abad Districts) to 280.57 (Bandar–e-Jask). Calculat- ed SIRs are presented in Fig. 2. The distribution of cases according to year and moth is shown in Fig. 3. As seen in this http://jad.tums.ac.ir/ J Arthropod-Borne Dis, March 2021, 15(1): 108–125 A Ghanbarnejad et al.: Spatial Modelling of … 111 http://jad.tums.ac.ir Published Online: March 31, 2021 figure, the trend of registered malaria cases over- ally declined from 2011 to 2018. Also, there is a peak in summer every year. The descriptive statistics of meteorological covariates are pre- sented in Table 2. As mentioned in the table, the variation of temperature is small because Hormozgan Province is located in a warm ar- ea, but the humidity has wide range variability from approximately 30% to 71%, this is be- cause of the diversity of land cover of area. In the study period, annual rainfall ranged from 5mm to 16.62mm. Model fitting The Bayesian Poisson-Gamma random field model was fitted to the data through MCMC and Gibbs sampling method with 200000 iter- ations after burning of first 50000 iterations. The regression coefficients and risk ratio of co- variates influence on malaria incidence with the 95% Bayesian credible intervals are re- ported in Table 1. As mentioned in Table 2, the effects of temperature and humidity on ma- laria incidence are positive although rainfall has negative impact on malaria incidence. The smoothed SIR for malaria incidence are esti- mated from the model and shown in Fig. 3. Table 1. Demographic characteristics of the patients with malaria in Hormozgan Province during 2011–2018 Variable Category No. (%) Sex Male 763 (86.5 %) Female 119 (13.5 %) Age Under 5 years 60 (6.8 %) 5–15 years 116 (13.2 %) 16–29 years 481 (54.5 %) 30–64 years 211 (23.9 %) 65 years and older 14 (1.6 %) Residency Urban 453 (51.4 %) Rural 429 (48.6 %) Nationality Iranian 163 (18.5 %) Afghan 270 (30.6 %) Pakistani 425 (48.2 %) Other Nations 24 (2.7 %) Job Worker 479 (54.3 %) Farmer 42 (4.8 %) Housewife 40 (4.5 %) School students 18 (2 %) Children 99 (11.2 %) Other 204 (23.1 %) Type of Malaria Parasites Plasmodium vivax 801 (90.8 %) Plasmodium falciparum 79 (9 %) Mixed 2 (0.2 %) Table 2. Descriptive statistics of meteorological covariates in Hormozgan Province during 2011–2018 Variables Mean Standard Deviation Range Temperature (°C) 28.04 0.86 26.09–29.76 Humidity (%) 52.70 14.41 29.50–70.71 Rainfall (mm/year) 10.14 4.17 5.07–16.62 http://jad.tums.ac.ir/ J Arthropod-Borne Dis, March 2021, 15(1): 108–125 A Ghanbarnejad et al.: Spatial Modelling of … 112 http://jad.tums.ac.ir Published Online: March 31, 2021 Fig. 1. Incidence rate of malaria in Hormozgan Province Fig. 2. Calculated standardized incidence ratio (SIR) for malaria in Hormozgan Province http://jad.tums.ac.ir/ J Arthropod-Borne Dis, March 2021, 15(1): 108–125 A Ghanbarnejad et al.: Spatial Modelling of … 113 http://jad.tums.ac.ir Published Online: March 31, 2021 Table 3. Summary statistics of coefficients of Bayesian Poisson-Gamma Model Variable RC* 95% CI** for RC RR*** 95% CI** for RR Temperature (⁰C) 0.8287 (0.65, 1.02) 2.29 1.92–2.78 Rainfall (mm/year) -0.079 (-0.107, - 0.05) 0.92 0.90–0.95 Humidity (%) 0.43 (0.029, 0.057) 1.04 1.03–1.06 Latent Source 0.0059 (0.0003, 0.02576) *Regression coefficients **Credible interval ***Risk Ratio Fig. 3. Trend of malaria cases in Hormozgan Province during 2011-Apr to 2018-Mar Fig. 4. Smoothed standardized incidence ratio (SIR) based on Bayesian Modelling http://jad.tums.ac.ir/ J Arthropod-Borne Dis, March 2021, 15(1): 108–125 A Ghanbarnejad et al.: Spatial Modelling of … 114 http://jad.tums.ac.ir Published Online: March 31, 2021 Discussion The key point of the present study was map- ping malaria incidence in a region which pre- viously known as endemic area for malaria in- fection and elimination program is implement- ed in the region. The estimated malaria risk map helps health policy makers to have better real- ization of the infection risk in the region and effect of climate. As mentioned in results, there is a decline of detected malaria cases from 2011 to 2018 because of good implementation of malaria elim- ination program and successful malaria surveil- lance system. In this paper we developed a model for an- alyze spatial pattern of malaria incidence in Hormozgan Province, south of Iran in pres- ence of some climate covariates. Visualizing standardized incidence rate (SIR) on the region map is one of the best represen- tation of disease burden in epidemiological stud- ies. In the Malaria disease, there are studies which produce smoothed maps via statistical modelling around the world such as works done by Taddese et al. in northwest of Ethiopia (26), Saita et al. in Tak province of Thailand (27), Bui et al. in Brazil (28), Gwitira et al. in Zim- babwe (29), Sasane et al. in India (30), Nur et al. in Indonesia (31), Hast et al. in Zambia (32). The Bayesian framework was used to mod- eling the malaria incidence and create smoothed map after adjusting for climate variables: tem- perature, rainfall and humidity. This is the first research that uses a Bayesian modeling frame- work to study the relationship between clima- tological covariates and malaria incidence in Hormozgan Province. The last published pa- per for mapping malaria in Hormozgan was con- ducted by Hanafi-Bojd in 2012 (33), they ana- lyzed data for only Bashagard District and pro- duced malaria risk maps and identified hot spots. Also, there is another published work in Minab District of Hormozgan Province that assessed the association between meteorological factors with malaria incidence during 2003 to 2009 us ing time series analysis but not considering the spatial pattern (34). Effect of temperature Based on Bayesian modelling, temperature was important environmental covariate on ma- laria incidence. The impact of malaria was haz- ardous due to the approximately 2.3 risk ratio. This result is consistent with the study by Mo- hammadkhani et al. in Kerman, southeast of Iran (35). Because of neighboring of Hormozgan and Kerman provinces this result is feasible. Also, Umer et al. in Pakistan evaluate the relation- ship between climate factors and malaria inci- dence and their findings were in line with our study. In the study by Ikeda et al. in South Af- rica (36), high temperature was associated with high incidence which was similar to the pre- sent study. Kang et al. in Madagascar showed that temperature is an affecting factor on ma- laria (37). Laneri et al. assess the impact of cli- mate drivers on malaria incidence in a region of Argentina and found that the temperature had positive effect (38) which is consistent with the present study. Sempiira et al. in Uganda ana- lyzed MIS data for children under 5 years and the effect of land surface temperature was pos- itive and significant (39). Herekar et al. in Ka- rachi, Pakistan found the positive correlation between temperature and malaria cases (40). Liu et al. in Tengchong County of Yunnan Prov- ince in China investigated the effect of climate on malaria incidence and had found that high temperature is an important factor on malaria (41). In some studies, there are negative impact on malaria incidence, M’Bra et al. found a neg- ative effect of temperature on malaria incidence in Cote D’Ivoire (42). The effect of temperature on malaria incidence was negative in a research by Santos-Vega et al. in northwest of India (43). Effect of humidity Another climate factor which has positive http://jad.tums.ac.ir/ J Arthropod-Borne Dis, March 2021, 15(1): 108–125 A Ghanbarnejad et al.: Spatial Modelling of … 115 http://jad.tums.ac.ir Published Online: March 31, 2021 and significant effect on malaria incidence in our study was humidity. This result is consistent with other studies around the world. Herekar et al. in Pakistan concluded the same result for humidity (40). Laneri et al. had found a com- plex relationship between humidity and malar- ia, in their study the maximum humidity above 85% had negative effect and minimum temper- ature had positive effect which is consistent with this study in some aspects. Simple et al. found a positive impact of humidity on malaria in Uganda (44) which is in line with our study. Effect of rainfall The impact of rainfall on malaria incidence was estimated to be negative in this study which is consistent with the study conducted by Adi- gun et al. in Nigeria (45). Millar et al. found that the rainy season was the strongest climate predictor for malaria (46). Matthew showed that there is a strong and direct correlation between rainfall and malaria occurrence in Ile-Ife region in south-western of Nigeria (47). But in our study the rainfall had negative impact on ma- laria incidence. The direct effect of rainfall has seen in the African countries which have trop- ical and wet climate (48) which is different from the climate of Hormozgan. The studied region has a warm and dry summer and in the winter, some of the area experienced rainy days. The spatial distribution in Hormozgan showed a hot spots pattern in the areas which have foreign workers from other provinces and other coun- tries. There is noticeable effect of temperature on malaria incidence which is concluded that the people should be aware in hot season and policy makers must have good planning to con- trol and eliminate the malaria in the district. Conclusion Based on the analysis of the study results, it was found that the ecological conditions of the region (temperature, humidity and rainfall) and population displacement play an important role in the incidence of malaria. Therefore, the malaria surveillance system should continue to be active in the region, focusing on high-risk areas of malaria. Country has a long history of work on malaria and publication of several pa- pers on different aspects of malaria including insecticide resistance monitoring, sibling spe- cies, molecular study, new record, novel meth- ods for vector control, faunestic study, use of plants for larval control, using bednets and long lasting impregnated nets, morphological stud- ies, malaria epidemiology, ecology of malaria vectors, biodiversity, community participation, vector control, repellent evaluation, anthro- pophilic index of malaria vectors, training is des- ignated as malaria training center by WHO. There are several reports on different aspects of malaria vectors recently (49-143). Acknowledgements The authors would like to express grati- tude to the staff of the Communicable Disease Center of Hormozgan University of Medical Sciences specially Mr Sajjad Fekri in gather- ing the data. 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