Weather integrated multiple machine learning models for prediction of dengue prevalence in India.

Dengue is a rapidly spreading viral disease transmitted to humans by Aedes mosquitoes. Due to global urbanization and climate change, the number of dengue cases are gradually increasing in recent decades. Hence, an early prediction of dengue continues to be a major concern for public health in countries with high prevalence of dengue. Creating a robust forecast model for the accurate prediction of dengue is a complex task and can be done through various data modelling approaches. In the present study, we have applied vector auto regression, generalized boosted models, support vector regression, and long short-term memory (LSTM) to predict the dengue prevalence in Kerala state of the Indian subcontinent. We consider the number of dengue cases as the target variable and weather variables viz., relative humidity, soil moisture, mean temperature, precipitation, and NINO3.4 as independent variables. Various analytical models have been applied on both datasets and predicted the dengue cases. Among all the models, the LSTM model was outperformed with superior prediction capability (RMSE: 0.345 and R2:0.86) than the other models. However, other models are able to capture the trend of dengue cases but failed in predicting the outbreak periods when compared to LSTM. The findings of this study will be helpful for public health agencies and policymakers to draw appropriate control measures before the onset of dengue. The proposed LSTM model for dengue prediction can be followed by other states of India as well.

Climate based malaria forecasting system for Andhra Pradesh, India.

Malaria is a major public health problem in tropical and subtropical countries of the World. During the year 1999, Visakhapatnam district of Andhra Pradesh, India experienced a major epidemic of malaria, and nearly 41,805 cases were reported. Hence, a retrospective malaria surveillance study was conducted from 2001 to 2016 and reported nearly a total of 149,317 malaria cases during the study period. Of which, Plasmodium vivax contributes 32%, and Plasmodium falciparum contributes 68% of the total cases. Malaria cases follow a strong seasonal variation and 70% of cases are reported during the monsoon periods. In the present study, we exploited multi step polynomial regression and seasonal autoregressive integrated moving average (SARIMA) models to forecast the malaria cases in the study area. The polynomial model predicted malaria cases with high predictive power and found that malaria cases at lag one, and population played a vital role in malaria transmission. Similarly, mean temperature, rainfall and Normalized Difference Vegetation Index build a significant impact on malaria cases. The best fit model was SARIMA (1, 1, 2) (2, 1, 1)12 which was used for forecasting monthly malaria incidence for the period of January 2015 to December 2016. The performance accuracy of both models are similar, however lowest Akaike information criterion score was observed by the polynomial model, and this approach can be helpful further for forecasting malaria incidence to implement effective control measures in advance for combating malaria in India.

Dengue situation in India: Suitability and transmission potential model for present and projected climate change scenarios.

Dengue fever is mosquito borne viral disease caused by dengue virus and transmitted by Aedes mosquitoes. In recent years the dengue has spread rapidly to several regions and it becomes a major public health menace globally. Dengue transmission is strongly influenced by environmental factors such as temperature and rainfall. In the present study, a climate driven dengue model was developed and predicted areas vulnerable for dengue transmission under the present and future climate change scenarios in India. The study also projected the dengue distribution risk map using representative concentration pathways (RCP4.5 and RCP8.5) in India in 2018-2030 (forthcoming period), 2031-2050 (intermediate period) and 2051-2080 (long period). The dengue cases assessed in India from 1998 to 2018 and found that the dengue transmission is gradually increasing year over year. The temperature data from 1980 to 2017 shows that, the mean temperatures are raising in the Southern region of India. During 2000-2017 periods the dengue transmission is steadily increasing across the India in compare with 1980-1999 periods. The dengue distribution risk is predicted and it is revealed that the coastal states have yearlong transmission possibility, but the high transmission potential is observed throughout the monsoon period. Due to the climate change, the expansion two more months of dengue transmission risk occurs in many regions of India. Both RCP4.5 and RCP8.5 scenarios revealed that dengue outbreaks might occur at larger volume in Southern, Eastern, and Central regions of India. Furthermore a sensitivity analysis was performed to explore the impact of climate change on dengue transmission. These results helps to suggest appropriate control measures should be implemented to limit the spread in future warmer climates. Besides these, a proper plan is required to mitigate greenhouse gas emissions to reduce the epidemic potential of dengue in India.

Applications of machine learning techniques to predict filariasis using socio-economic factors.

Filariasis is one of the major public health concerns in India. Approximately 600 million people spread across 250 districts of India are at risk of filariasis. To predict this disease, a pilot scale study was carried out in 30 villages of Karimnagar district of Telangana from 2004 to 2007 to collect epidemiological and socio-economic data. The collected data are analysed by employing various machine learning techniques such as Naïve Bayes (NB), logistic model tree, probabilistic neural network, J48 (C4.5), classification and regression tree, JRip and gradient boosting machine. The performances of these algorithms are reported using sensitivity, specificity, accuracy and area under ROC curve (AUC). Among all employed classification methods, NB yielded the best AUC of 64% and was equally statistically significant with the rest of the classifiers. Similarly, the J48 algorithm generated 23 decision rules that help in developing an early warning system to implement better prevention and control efforts in the management of filariasis.

Lag effect of climatic variables on dengue burden in India.

Dengue is a widespread vector-borne disease believed to affect between 100 and 390 million people every year. The interaction between vector, host and pathogen is influenced by various climatic factors and the relationship between dengue and climatic conditions has been poorly explored in India. This study explores the relationship between El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and dengue cases in India. Additionally, distributed lag non-linear model was used to assess the delayed effects of climatic factors on dengue cases. The weekly dengue cases reported by the Integrated Disease Surveillance Program (IDSP) over India during the period 2010-2017 were analysed. The study shows that dengue cases usually follow a seasonal pattern, with most cases reported in August and September. Both temperature and rainfall were positively associated with the number of dengue cases. The precipitation shows the higher transmission risk of dengue was observed between 8 and 15 weeks of lag. The highest relative risk (RR) of dengue was observed at 60 mm rainfall with a 12-week lag period when compared with 40 and 80 mm rainfall. The RR of dengue tends to increase with increasing mean temperature above 24 °C. The largest transmission risk of dengue was observed at 30 °C with a 0-3 weeks of lag. Similarly, the transmission risk increases more than twofold when the minimum temperature reaches 26 °C with a 2-week lag period. The dengue cases and El Niño were positively correlated with a 3-6 months lag period. The significant correlation observed between the IOD and dengue cases was shown for a 0-2 months lag period.

Temperature dependent transmission potential model for chikungunya in India.

Chikungunya is a major public health problem in tropical and subtropical countries of the world. During 2016, the National Capital Territory of Delhi experienced an epidemic caused by chikungunya virus with >12,000 cases. Similarly, other parts of India also reported a large number of chikungunya cases, highest incidence rate was observed during 2016 in comparison with last 10 years of epidemiological data. In the present study we exploited R0 mathematical model to understand the transmission risk of chikungunya virus which is transmitted by Aedes vectors. This mechanistic transmission model is climate driven and it predicts how the probability and transmission risk of chikungunya occurs in India. The gridded temperature data from 1948 to 2016 shows that the mean temperatures are gradually increasing in South India from 1982 to 2016 when compared with data of 1948-1981 time scale. During 1982-2016 period many states have reported gradual increase in risk of chikungunya transmission when compared with the 1948-1981 period. The highest transmission risk of chikungunya in India due to favourable ecoclimatic conditions, increasing temperature leads to low extrinsic incubation period, mortality rates and high biting rate were predicted for the year 2016. The epidemics in 2010 and 2016 are also strongly connected to El Nino conditions which favours transmission of chikungunya in India. The study shows that transmission of chikungunya occurs between 20 and 34 °C but the peak transmission occurs at 29 °C. The infections of chikungunya in India are due to availability of vectors and optimum temperature conditions influence chikungunya transmission faster in India. This climate based empirical model helps the public health authorities to assess the risk of chikungunya and one can implement necessary control measures before onset of disease outbreak.

Influence of socioeconomic aspects on lymphatic filariasis: A case-control study in Andhra Pradesh, India.

BACKGROUND & OBJECTIVES: Lymphatic filariasis (LF) is a major public health problem in India. The objective of the study was to assess the impact of socioeconomic conditions on LF in Chittoor district of Andhra Pradesh, India. METHODS: A survey was carried out from 2004 to 2007 during which, an epidemiological and socioeconomic data were collected and analysed. The microfilaria (mf) positive samples were taken as cases and matched with control group by sex and age (1:1) for case-control study. Bivariate and multivariate logistic regression was used to identify the potential risk factors for filariasis. Using principal component analysis (PCA), a socioeconomic index was developed and the data/scores were classified into low, medium and high categories. RESULTS: In total 5,133 blood smears were collected, of which 77 samples were found positive for microfilaria (1.52%). Multivariate analysis showed that the risk of filariasis was higher in groups of people with income < ₹1000 per month [OR = 2.752 (95%CI, 0.435-17.429)]; ₹ 1000-3000 per month [3.079 (0.923-0.275)]; people living in tiled house structure [1.641 (0.534-5.048)], with kutcha (uncemented) drainage system [19.427 (2.985- 126.410)], respondents who did not implemented mosquito avoidance measures [1.737 (0.563-5.358)]; and in people who were not aware about prevention and control of filariasis [1.042 (0.368-2.956)]. PCA showed that respondents with low (41.6%) and medium (33.8%) socioeconomic status are more prone to filariasis (p=0.036). INTERPRETATION & CONCLUSION: The cross sectional study showed that the population with low and medium socioeconomic status are at higher risk of filariasis. The identified socioeconomic risk factors can be used as a guideline for improving the conditions for effective management of filariasis.

Label-free kinetic analysis of an antibody-antigen interaction using biolayer interferometry.

Biolayer Interferometry (BLI) is a powerful technique that enables direct measurement of biomolecular interactions in real time without the need for labeled reagents. Here we describe the analysis of a high-affinity binding interaction between a monoclonal antibody and purified antigen using BLI. A simple Dip-and-Read™ format in which biosensors are dipped into microplate wells containing purified or complex samples provides a highly parallel, user-friendly technique to study molecular interactions. A rapid rise in publications citing the use of BLI technology in a wide range of applications, from biopharmaceutical discovery to infectious diseases monitoring, suggests broad utility of this technology in the life sciences.

Data base management system for lymphatic filariasis--a neglected tropical disease.

BACKGROUND: Researchers working in the area of Public Health are being confronted with large volumes of data on various aspects of entomology and epidemiology. To obtain the relevant information out of these data requires particular database management system. In this paper, we have described about the usages of our developed database on lymphatic filariasis. METHODS: This database application is developed using Model View Controller (MVC) architecture, with MySQL as database and a web based interface. We have collected and incorporated the data on filariasis in the database from Karimnagar, Chittoor, East and West Godavari districts of Andhra Pradesh, India. CONCLUSION: The importance of this database is to store the collected data, retrieve the information and produce various combinational reports on filarial aspects which in turn will help the public health officials to understand the burden of disease in a particular locality. This information is likely to have an imperative role on decision making for effective control of filarial disease and integrated vector management operations.

A cohort study of lymphatic filariasis on socio economic conditions in Andhra Pradesh, India.

BACKGROUND: To assess the impact of socioeconomic variables on lymphatic filariasis in endemic villages of Karimnagar district, Andhra Pradesh, India. METHODS: A pilot scale study was conducted in 30 villages of Karimnagar district from 2004 to 2007. These villages were selected based on previous reports from department of health, Government of Andhra Pradesh, epidemiology, entomology and socioeconomic survey was conducted as per protocol. Collected data were analysed statistically by Chi square test, Principal Component Analysis, Odds ratio, Bivariate, multivariate logistic regression analysis. RESULTS: Total of 5,394 blood samples collected and screened for microfilaria, out of which 199 were found to be positive (3.7%). The socioeconomic data of these respondents/participants were correlated with MF prevalence. The socioeconomic variables like educational status (Odds Ratio (OR) = 2.6, 95% Confidence Interval (CI) = 1.1-6.5), house structure (hut OR = 1.9, 95% CI = 1.2-3.1; tiled OR = 1.3, 95% CI = 0.8-2) and participation in mass drug administration program (OR = 1.8, 95% CI = 1.3-2.6) were found to be highly associated with the occurrence of filarial disease. The socioeconomic index was categorized into low (3.6%; OR-1.1, 95% CI: 0.7-1.5) medium (4.9%; OR-1.5, 95% CI = 1-2.1) and high (3.3%) in relation to percentage of filarial parasite prevalence. A significant difference was observed among these three groups while comparing the number of cases of filaria with the type of socioeconomic conditions of the respondents (P = 0.067). CONCLUSIONS: From this study it is inferred that age, education of family, type of house structure and awareness about the filarial disease directly influenced the disease prevalence. Beside annual mass drug administration program, such type of analysis should be undertaken by health officials to target a few socioeconomic factors to reduce the disease burden. Health education campaigns in the endemic villages and imparting of protection measures against mosquitoes using insecticide treated bed nets would substantially reduce the disease in these villages.

Filaria monitoring visualization system: a geographical information system-based application to manage lymphatic filariasis in Andhra Pradesh, India.

Among various public health diseases, filariasis constitutes a major public health problem in India, wherein an estimated 553.7 million people are at risk of infection. The aim of this article is to present a spatial mapping and analysis of filariasis data over a 3-year period (2004-2007) from Karimnagar, Chittoor, East and West Godavari districts of Andhra Pradesh, India. The data include epidemiological and entomological studies (i.e., infection rate, infectivity rate, mosquito per man hour, and microfilaria rate). These parameters were customized on Geographical Information System (GIS) platform and developed filaria monitoring visualization system (FMVS) for identifying the endemic/risk areas of filariasis among these four districts. GIS map for filariasis transmission from the study areas was created and stratified into different spatial entities like low, medium, and high risk zones. On the basis of the data and FMVS maps, it was demonstrated that filariasis remained unevenly distributed within the districts. Balancing the intervention coverage in different villages with overall mass drug administration and continued promotion of the proper use of control measures are necessary for further reduction of filarial cases in these districts.

Fluorescent-conjugated polymer superquenching facilitates highly sensitive detection of proteases.

Sensor formats have been developed for detecting the activity of proteolytic enzymes based on fluorescent conjugated polymer superquenching. These sensors employ a reactive peptide sequence within a tether linking a quencher to a biotin. The peptide binds to sensors containing colocated biotin-binding protein and fluorescent polymer by means of biotin-biotin binding protein interactions, resulting in a strong quenching of polymer fluorescence. Enzyme-mediated cleavage of the peptide results in a reversal of the fluorescence quenching. These assays for protease activity are simple, sensitive, fast, and have the specificity required for screening chemical libraries for novel protease inhibitors in a high-throughput screening assay environment. These assays have been demonstrated for enterokinase, caspase-3/7, and beta-secretase.