Historically, malaria in India was predominantly caused by Plasmodium vivax, accounting for 53% of the estimated cases. After the spread of drug-resistant Plasmodium falciparum in the 1990s, the prevalence of the two species remained equivalent at the national level for a decade. By 2014, the proportion of P. vivax has decreased to 34% nationally, but with high regional variation. In 2014, P. vivax accounted for around 380,000 malaria cases in India; almost a sixth of all P. vivax cases reported globally. Plasmodium vivax has remained resistant to control measures, particularly in urban areas. Urban malaria is predominantly caused by P. vivax and is subject to outbreaks, often associated with increased mortality, and triggered by bursts of migration and construction. The epidemiology of P. vivax varies substantially within India, including multiple relapse phenotypes with varying latencies between primary infection and relapse. Moreover, the hypnozoite reservoir maintains transmission potential and enables reestablishment of the parasite in areas in which it was thought eradicated. The burden of malaria in India is complex because of the highly variable malaria eco-epidemiological profiles, transmission factors, and the presence of multiple Plasmodium species and Anopheles vectors. This review of P. vivax malaria in India describes epidemiological trends with particular attention to four states: Gujarat, Karnataka, Haryana, and Odisha.

BackgroundThe focus of India’s National Malaria Programme witnessed a paradigm shift recently from health facility to community-based approaches. The current thrust is on diagnosing and treating malaria by community health workers and prevention through free provision of long-lasting insecticidal nets. However, appropriate community awareness and practice are inevitable for the effectiveness of such efforts. In this context, the study assessed community perceptions and practice on malaria and similar febrile illnesses. This evidence base is intended to direct the roll-out of the new strategies and improve community acceptance and utilization of services.MethodsA qualitative study involving 26 focus group discussions and 40 key informant interviews was conducted in two districts of Odisha State in India. The key points of discussion were centred on community perceptions and practice regarding malaria prevention and treatment. Thematic analysis of data was performed.ResultsThe 272 respondents consisted of 50% females, three-quarter scheduled tribe community and 30% students. A half of them were literates. Malaria was reported to be the most common disease in their settings with multiple modes of transmission by the FGD participants. Adoption of prevention methods was seasonal with perceived mosquito density. The reported use of bed nets was low and the utilization was determined by seasonality, affordability, intoxication and alternate uses of nets. Although respondents were aware of malaria-related symptoms, care-seeking from traditional healers and unqualified providers was prevalent. The respondents expressed lack of trust in the community health workers due to frequent drug stock-outs. The major determinants of health care seeking were socio-cultural beliefs, age, gender, faith in the service provider, proximity, poverty, and perceived effectiveness of available services.ConclusionApart from the socio-cultural and behavioural factors, the availability of acceptable care can modulate the community perceptions and practices on malaria management. The current community awareness on symptoms of malaria and prevention is fair, yet the prevention and treatment practices are not optimal. Promoting active community involvement and ownership in malaria control and management through strengthening community based organizations would be relevant. Further, timely availability of drugs and commodities at the community level can improve their confidence in the public health system.

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BackgroundAlthough Odisha is the largest contributor to the malaria burden in India, no systematic study has examined its malaria trends. Hence, the spatio-temporal trends in malaria in Odisha were assessed against the backdrop of the various anti-malaria strategies implemented in the state.MethodsUsing the district-wise malaria incidence and blood examination data (2003–2013) from the National Vector Borne Disease Control Program, blood examination-adjusted time-trends in malaria incidence were estimated and predicted for 2003–2013 and 2014–2016, respectively. An interrupted time series analysis using segmented regression was conducted to compare the disease trends between the pre (2003–2007) and post-intensification (2009–2013) periods. Key-informant interviews of state stakeholders were used to collect the information on the various anti-malaria strategies adopted in the state.ResultsThe state annual malaria incidence declined from 10.82/1000 to 5.28/1000 during 2003–2013 (adjusted annual decline: -0.54/1000, 95% CI: -0.78 to -0.30). However, the annual blood examination rate remained almost unchanged from 11.25% to 11.77%. The keyinformants revealed that intensification of anti-malaria activities in 2008 led to a more rapid decline in malaria incidence during 2009–2013 as compared to that in 2003–2007 [adjusted decline: -0.83 (-1.30 to -0.37) and -0.27 (-0.41 to -0.13), respectively]. There was a significant difference in the two temporal slopes, i.e., -0.054 (-0.10 to -0.002, p = 0.04) per 1000 population per month, between these two periods, indicating almost a 200% greater decline in the post-intensification period. Although, the seven southern high-burden districts registered the highest decline, they continued to remain in that zone, thereby, making the achievement of malaria elimination (incidence <1/1000) unlikely by 2017.ConclusionThe anti-malaria strategies in Odisha, especially their intensification since 2008, have helped improve its malaria situation in recent years. These successful measures need to be sustained and perhaps intensified further for eliminating malaria from Odisha.

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The prevalence of malaria in India is decreasing, but it remains a major concern for public health administration. The role of submicroscopic malaria and asymptomatic malaria parasitemia and their persistence is being explored. A cross-sectional survey was conducted in the Kandhamal district of Odisha (India) during May-June 2017. Blood samples were collected from 1897 individuals for screening of asymptomatic parasitemia. Samples were screened using rapid diagnostic tests (RDTs) and examined microscopically for Plasmodium species. Approximately 30% of randomly selected samples (n = 586) were analyzed using real-time PCR (qPCR), and the genetic diversity of Plasmodium falciparum was analyzed. The prevalence of Plasmodium species among asymptomatic individuals detected using qPCR was 18%, which was significantly higher than that detected by microscopy examination (5.5%) or RDT (7.3%). Of these, 37% had submicroscopic malaria. The species-specific prevalence among asymptomatic malaria-positive cases for P. falciparum, Plasmodium vivax, and mixed infection (P. falciparum and P. vivax) by qPCR was 57%, 29%, and 14%, respectively. The multiplicity of infection was 1.6 and 1.2 for the merozoite surface protein-1 gene (msp1) and (msp2), respectively. Expected heterozygosity was 0.64 and 0.47 for msp1 and msp2, respectively. A significant proportion of the study population, 105/586 (18%), was found to be a reservoir for malaria infection, and identification of this group will help in the development of elimination strategies.

An Elman network is used for the prediction of material removal rate (MRR) in electrical discharge machining (EDM). An Elman network is a dynamic recurrent neural network that can be used to model non-linear dynamic systems. Training of the models is performed with data from series of EDM experiments on AISI D2 tool steel from finishing, semi-finish to roughing operations. The machining parameters such as discharge current, pulse duration, duty cycle, and voltage were used as model input variables during the development of predictive models. The developed model is validated with a new set of experimental data that was not used for the training step. The mean percentage error of the model is found to be less than 6 per cent, which shows that the proposed model can satisfactorily predict the MRR in EDM.

In this work, two different artificial neural network (ANN) models -back-propagation neural network (BPN) and radial basis function neural network (RBFN) -are presented for the prediction of surface roughness in die sinking electrical discharge machining (EDM). The pulse current (Ip), the pulse duration (Ton), and duty cycle (t) are chosen as input variables with a constant voltage of 50 volt, and surface roughness is the output parameters of the model. A widespread series of EDM experiments was conducted on AISI D2 steel to acquire the data for training and testing and it was found that the neural models could predict the process performance with reasonable accuracy, under varying machining conditions. However, RBFN is faster than the BPNs and the BPN is reasonably more accurate. Moreover, they can be considered as valuable tools for EDM, by giving reliable predictions and provide a possible way to avoid timeand money-consuming experiments.