Molecular Epidemiology of Plasmodium species in Conflicted Federally Administered Tribal Area (FATA) Pakistan

Military conflicts have been significant obstacles in detecting and treating infectious disease diseases due to the diminished public health infrastructure, resulting in malaria endemicity. A variety of violent and destructive incidents were experienced by FATA (Federally Administered Tribal Areas). It was a struggle to pursue an epidemiological analysis due to continuing conflict and Talibanization. Clinical isolates were collected from Bajaur, Mohmand, Khyber, Orakzai agencies from May 2017 to May 2018. For Giemsa staining, full blood EDTA blood samples have been collected from symptomatic participants. Malaria-positive microscopy isolates were spotted on filter papers for future Plasmodial molecular detection by nested polymerase chain reaction (nPCR) of small subunit ribosomal ribonucleic acid (ssrRNA) genes specific primers. Since reconfirming the nPCR, a malariometric study of 762 patients found 679 positive malaria cases. Plasmodium vivax was 523 (77%), Plasmodium falciparum 121 (18%), 35 (5%) were with mixed-species infection (P. vivax plus P. falciparum), and 83 were declared negative by PCR. Among the five agencies of FATA, Khyber agency has the highest malaria incidence (19%) with followed by P. vivax (19%) and P. falciparum (4.1%). In contrast, Kurram has about (14%), including (10.8%) P. vivax and (2.7%) P. falciparum cases, the lowest malaria epidemiology. Surprisingly, no significant differences in the distribution of mixed-species infection among all five agencies. P. falciparum and P. vivax were two prevalent FATA malaria species in Pakistan’s war-torn area. To overcome this rising incidence of malaria, this study recommends that initiating malaria awareness campaigns in school should be supported by public health agencies and malaria-related education locally, targeting children and parents alike.

Using the Hurst Exponent and Entropy Measures to Predict Effective Transmissibility in Empirical Series of Malaria Incidence

We analyze the empirical series of malaria incidence, using the concepts of autocorrelation, Hurst exponent and Shannon entropy with the aim of uncovering hidden variables in those series. From the simulations of an agent model for malaria spreading, we first derive models of the malaria incidence, the Hurst exponent and the entropy as functions of gametocytemia, measuring the infectious power of a mosquito to a human host. Second, upon estimating the values of three observables—incidence, Hurst exponent and entropy—from the data set of different malaria empirical series we predict a value of the gametocytemia for each observable. Finally, we show that the independent predictions show considerable consistency with only a few exceptions which are discussed in further detail.

Spatial Patterns and Climate Drivers of Malaria in Three Border Areas of Brazil, Venezuela and Guyana, 2016-2018

IntroductionIn 2020, 77% of malaria cases in the Americas were concentrated in Venezuela, Brazil, and Colombia. These countries are characterized by a heterogeneous malaria landscape and malaria hotspots. Furthermore, the political unrest in Venezuela has led to significant cross-border population movement. Hence, the aim of this study was to describe spatial patterns and identify significant climatic drivers of malaria transmission along the Venezuela-Brazil-Guyana border, focusing on Bolivar state, Venezuela and Roraima state, Brazil.MethodsMalaria case data, stratified by species from 2016-2018, were obtained from the Brazilian Malaria Epidemiology Surveillance Information System, the Guyana Vector Borne Diseases Program, the Venezuelan Ministry of Health, and civil society organizations. Spatial autocorrelation in malaria incidence was explored using Getis-Ord (Gi*) statistics. A Poisson regression model was developed with a conditional autoregressive prior structure and posterior parameters were estimated using the Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. Climatic covariates were precipitation and minimum and maximum temperature. ResultsThere were 685,498 malaria cases during the study period. Plasmodium vivax was the predominant species (71.7%, 490,861). Malaria hotspots were located in eight municipalities along the Venezuela and Guyana international borders with Brazil. Plasmodium falciparum decreased by 1.6% (95% credible interval [CrI] 1.5%, 2.3%) and 9.6% (95% CrI 1.5%, 25.2%) per 1 cm increase in six-month lagged precipitation and each 1°C increase of minimum temperature without lag. Each 1°C increase of one-month lagged maximum temperature increased P. falciparum by 6.6% (95% CrI 4.8%, 21.7%). P. vivax cases decreased by 1.0% (95% CrI 1.0%, 1.1%) and 7.0% (95% CrI 6.5%, 7.5%) for each 1 cm increase of precipitation lagged at six-months and 1°C increase in minimum temperature lagged at six-months. There was no significant residual spatial clustering after accounting for climatic covariates.ConclusionMalaria hotspots were located along the Venezuela and Guyana international border with Roraima state, Brazil. In addition to population movement, climatic variables are important drivers of malaria transmission in these areas.

Impact of The COVID-19 Pandemic on the Malaria Burden in Northern Ghana: Analysis of Routine Surveillance Data

Introduction: The COVID-19 pandemic and its collateral damage severely impact health systems globally and risk to worsen the malaria situation in endemic countries. Malaria is a leading cause of morbidity and mortality in Ghana. This study aims to analyze routine surveillance data to assess possible effects on the malaria burden in the first year of the COVID-19 pandemic in the Northern Region of Ghana. Methods: Monthly routine data from the District Health Information Management System II (DHIMS2) of the Northern Region of Ghana were analyzed. Overall outpatient department visits and malaria incidence rates from the years 2015 to 2019 were compared to the corresponding data of the year 2020. Results: Compared to the corresponding periods of the years 2015 to 2019, overall visits and malaria incidence in pediatric and adult outpatient departments in northern Ghana decreased in March and April 2020, when major movement and social restrictions were implemented in response to the pandemic. Incidence slightly rebounded afterwards in 2020 but stayed below the average of the previous years. Data from inpatient departments showed a similar but more pronounced trend when compared to outpatient departments. In pregnant women, however, malaria incidence in outpatient departments increased after the first COVID-19 wave. Discussion: The findings from this study show that the COVID-19 pandemic affects the malaria burden in health facilities of Ghana, with declines in in- and outpatient rates. Pregnant women may experience reduced access to intermittent preventive malaria treatment and insecticide treated nets, resulting in subsequent higher malaria morbidity. Further data from other African countries, particularly on community-based studies, are needed to fully determine the impact of the pandemic on the malaria situation.

Strong off-target antibody reactivity to malarial antigens induced by RTS,S/AS01E vaccination is associated with increased protection

The RTS,S/AS01E vaccine targets the circumsporozoite protein (CSP) of the Plasmodium falciparum parasite. Using protein microarrays, levels of IgG to 1,000 P. falciparum antigens were measured in 2,138 infants (age 6-12 weeks) and children (age 5-17 months) from 6 African sites of the phase 3 trial, sampled before and at four longitudinal visits after vaccination. One month post-vaccination, IgG responses to 17% of all probed antigens showed differences between RTS,S/AS01E and comparator vaccination groups, whereas no prevaccination differences were found. A small subset of antigens presented IgG levels reaching 4- to 8 fold increases in the RTS,S/AS01E group, comparable in magnitude to anti-CSP IgG levels (~11-fold increase). They were strongly cross-correlated and correlated with anti CSP levels, waning similarly over time and re-increasing with the booster dose. Such an intriguing phenomenon may be due to cross-reactivity of anti-CSP antibodies with these antigens. RTS,S/AS01E vaccinees with strong off target IgG responses had an estimated lower clinical malaria incidence after adjusting for age group, site and post-vaccination anti-CSP levels. RTS,S/AS01E-induced IgG may bind strongly not only to CSP, but to unrelated malaria antigens, and this seems to either confer, or at least be a marker of, increased protection from clinical malaria.

Cross-Border Malaria Drivers and Risk Factors on the Brazil-Venezuela Border Between 2016 and 2018

Background Globally, cross-border importation of malaria has become a challenge to malaria elimination. The border areas between Brazil and Venezuela have experienced high numbers of imported cases due to increased population movement and migration out of Venezuela. This study aimed to identify risk factors for imported malaria and delineate imported malaria hotspots in Roraima, Brazil and Bolivar, Venezuela between 2016 and 2018.MethodsData on malaria surveillance cases from Roraima, Brazil and Bolivar, Venezuela from 2016 to 2018 were obtained from national surveillance systems: the Brazilian Malaria Epidemiology Surveillance Information System (SIVEP-Malaria), the Venezuelan Ministry of Health and other non-government organizations. A multivariable logistic regression model was used to identify the risk factors for imported malaria. Spatial autocorrelation in malaria incidence was explored using Getis-Ord (Gi*) statistics.ResultsDuring the study period, there were 11,270 (24.3%) and 4,072 (0.7%) imported malaria cases in Roraima, Brazil and Bolivar, Venezuela, respectively. In the multivariable logistic regression for Roraima, men were 28% less likely to be an imported case compared to women (Adjusted Odds Ratio [AOR]= 0.72; 95% confidence interval [CI] 0.665, 0.781). Ages 20-29 and 30-39 were 90% (AOR=1.90; 95% CI 1.649, 2.181) and 54% (AOR=1.54; 95% CI 1.331, 1.782) more likely to be an imported case compared to the 0-9 year age group, respectively. Imported cases were 197 times (AOR=197.03; 95% CI 175.094, 221.712) more likely to occur in miners than those working in agriculture and domestic work. In Bolivar, cases aged 10-19 (AOR=1.75; 95% CI 1.389, 2.192), 20-29 (AOR=2.48; 95% CI 1.957, 3.144), and 30-39 (AOR=2.29; 95% CI 1.803, 2.913) were at higher risk of being an imported case than those in the 0-9 year old group, with older age groups having a slightly higher risk compared to Roraima. Compared to agriculture and domestic workers, tourism, timber and fishing workers (AOR=6.38; 95% CI 4.393, 9.254) and miners (AOR=7.03; 95% CI 4.903, 10.092) were between six and seven times more likely to be an imported case. Spatial analysis showed the risk was higher along the international border in the municipalities of Roraima, Brazil.ConclusionTo achieve malaria elimination, cross-border populations in the hotspot municipalities will need targeted intervention strategies tailored to occupation, age and mobility status. Furthermore, all stakeholders, including implementers, policymakers, and donors, should support and explore the introduction of novel approaches to address these hard-to-reach populations with the most cost-effective interventions.

Lagged effects of rainfall on malaria: a case study of Meghalaya

Background: Meghalaya contributes about twenty per cent of India's total malaria death and is one of the high malaria endemic states in India, very susceptible to malaria transmission mainly due to favorable climatic conditions that mostly facilitate the transmission. In the relationship between malaria and meteorological factors, existing studies mainly focus on the interaction between different climatic factors, while interaction within one specific climatic predictor at different ag times has been largely neglected. This paper aims to explore the interaction of lagged rainfalls and their impact on malaria incidence. Methods: The district monthly malaria records from Jan 2005 to December 2017 was collected from the Department of Health Services (Malaria), Government of Meghalaya. The district monthly meteorological records from Jan 2005 to December 2017 was collected from the Directorate of Agriculture, Government of Meghalaya, in which average temperature (℃), humidity (%) and rainfall (mm) had been recorded. Monthly malaria cases and three climatic variables of 4 districts in Meghalaya from 2015 to 2017 were analysed with the varying coefficient-distributed lag non-linear model. The missing climatic values were imputed using Kalman Smoothing on structural time series using the package imputeTS in R. Results: During the period 2005-2017, a total of 309133 malaria cases were reported in all the districts under study. The monthly average rainfall ranges from a minimum of 181.79 mm in South Garo to a maximum of 367.87 in Jaintia. Also, South Garo and East Khasi are the hottest and the coolest place understudy with 26.96 and 16.86 degrees Celsius respectively. Rainfall levels in the first-month lag affect the non-linear patterns between the incidence of malaria and rainfall at each lag time. The low rainfall level at the first-month lag may promote malaria incidence as rainfall increases. However, for the high rainfall level at the first-month lag, malaria incidence decreases as rainfall increases. Conclusion: The interaction effect between lagged rainfalls on malaria incidence was observed in this study, and highlights its importance for future studies to better understand and predict malaria transmission.

The Trend of Malaria Cases, Positivity Rate, and Determinant Factors in the Amhara Regional State, Ethiopia: A Mixed Method

Objectives. The objectives of this study were to evaluate the trend of malaria cases and test positivity rate and explore determinant factors in the Amhara Regional State, Ethiopia. Methods. A mixed study design (retrospective record data review and case study) was employed among 67 malaria officers from all zones in the region by using proportional allocation and the 1995 to 2020 malaria document review. 1995 to 2020 trend analysis was conducted using RStudio-1.2.5033. Vignette Focus Group Discussions (FGDs) were used to dig the possible factors for malaria case buildup using the purposive sampling technique, and a qualitative content analysis was used. Results. The overall mean test positivity rate (TPR) was 21.9%, and about 80% of the land of the region was malarious, and 68% of the population was at risk of malaria in the study area from the data records of 1995 to 2020. The year 2012 to 2016 had the peak confirmed malaria cases, while the year 2016 to 2018 dramatically reduced followed by an increase in 2019/2020. The vignette FGDs identified that poor performance on Larval Source Management (LSM) and net utilization, no stock of some antimalarial medicine and supply, quality of malaria diagnosis services, the low commitment of leaders, and climatic anomalies facilitated surge of the disease in 2019/2020. No real accountability at all levels, low coverage of targeted vector control interventions, resource constraint, data quality and use for informed decision making, security issues and Internally Displaced Population (IDP) in various parts of the country, and the COVID-19 pandemic were the possible causes for case buildup. Conclusions. This result revealed that the malaria incidence rate showed a remarkable decline. However, the average TPR was 21.9%. Hence, it provided the ongoing feedback, mass fever test and treatment, training to health professionals, and ongoing supportive supervision (SS) and mentorship, improved net utilization and indoor residual spraying (IRS) operation and close follow-up and conducted sensitization workshop, spot messages were transferred through mass media, and temporary case treatment and prevention centers at farm sites established may surpass the threshold of malaria.

MALARIA PREDICTION MODEL USING ADVANCED ENSEMBLE MACHINE LEARNING TECHNIQUES

Malaria is a life-threatening disease that leads to death globally, its early prediction is necessary for preventing the rapid transmission. In this work, an enhanced ensemble learning approach for predicting malaria outbreaks is suggested. Using a mean-based splitting strategy, the dataset is randomly partitioned into smaller groups. The splits are then modelled using a classification and regression tree, and an accuracy-based weighted aging classifier ensemble is used to construct a homogenous ensemble from the several Classification and Regression Tree models. This approach ensures higher performance is achieved. Seven different Algorithms were tested and one ensemble method is used which combines all the seven classifiers together and finally, the accuracy, precision, and sensitivity achieved for the proposed method is 93%, 92%, and 100% respectively, which outperformed better than machine learning classifiers and ensemble method used in this research. The correlation between the variables used is established and how each factor contributes to the malaria incidence. The result indicates that malaria outbreaks can be predicted successfully using the suggested technique.

Oceanic Influence on Seasonal Malaria Incidence in West Africa.

Climate variability is a key factor in driving malaria outbreaks. As shown in previous studies, climate-driven malaria modeling provides a better understanding of malaria transmission dynamics, generating malaria-related parameters validated as a reliable benchmark to assess the impact of climate on malaria. In this framework, the present study uses climate observations and reanalysis products to evaluate the predictability of malaria incidence in West Africa. Sea surface temperatures (SSTs) are shown as a skillful predictor of malaria incidence, which is derived from climate-driven simulations with the Liverpool Malaria Model (LMM). Using the S4CAST tool, we find robust modes of anomalous SST variability associated with skillful predictability of malaria incidence Accordingly, significant SST anomalies in the tropical Pacific and Atlantic Ocean basins are related to a significant response of malaria incidence over West Africa. For the Mediterranean Sea, warm (cold) SST anomalies are responsible for increased (decreased) surface air temperatures and precipitation over West Africa, resulting in higher (lower) malaria incidence. Our results put forward the key role of SST variability as a source of predictability of malaria incidence, being of paramount interest to decision-makers who plan public health measures against malaria in West Africa. Accordingly, SST anomalies could be used operationally to forecast malaria risk over West Africa for early warning systems.