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EXPLORE IDM’S CURRENT RESEARCH PUBLICATIONS

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Alain Vandormael, Adam Akullian, Mark Siedner, Tulio de Oliveira, Till Bärnighausen and Frank Tanser

NATURE COMMUNICATIONS

Over the past decade, there has been a massive scale-up of primary and secondary prevention services to reduce the population-wide incidence of HIV. However, the impact of these services on HIV incidence has not been demonstrated using a prospectively followed, population-based cohort from South Africa—the country with the world’s highest rate of new infections. To quantify HIV incidence trends in a hyperendemic population, we tested a cohort of 22,239 uninfected participants over 92,877 person-years of observation. We report a 43% decline in the overall incidence rate between 2012 and 2017, from 4.0 to 2.3 seroconversion events per 100 person-years. Men experienced an earlier and larger incidence decline than women (59% vs. 37% reduction), which is consistent with male circumcision scale-up and higher levels of female antiretroviral therapy coverage. Additional efforts are needed to get more men onto consistent, suppressive treatment so that new HIV infections can be reduced among women.

Emmanuel P. Mwanga, Elihaika G. Minja, Emmanuel Mrimi, Mario González Jiménez, Johnson K. Swai, Said Abbasi, Halfan S. Ngowo, Doreen J. Siria, Salum Mapua, Caleb Stica, Marta F. Maia, Ally Olotu, Maggy T. Sikulu-Lord, Francesco Baldini, Heather M. Ferguson, Klaas Wynne, Prashanth Selvaraj, Simon A. Babayan & Fredros O. Okumu

MALARIA JOURNAL

Background

Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots.

Methods

Filter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in 12 wards in southeastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range 4000 cm−1 to 500 cm−1. The spectra were cleaned to compensate for atmospheric water vapour and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test-stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS.

Results

Logistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7%; sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and Plasmodium ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen.

Conclusion

These results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in human DBS. The approach could have potential for rapid and high-throughput screening of Plasmodium in both non-clinical settings (e.g., field surveys) and clinical settings (diagnosis to aid case management). However, before the approach can be used, we need additional field validation in other study sites with different parasite populations, and in-depth evaluation of the biological basis of the MIR signals. Improving the classification algorithms, and model training on larger datasets could also improve specificity and sensitivity. The MIR-ML spectroscopy system is physically robust, low-cost, and requires minimum maintenance.

CMSB

The compartmental modeling software (CMS) is an open source computational framework that can simulate discrete, stochastic reaction models which are often utilized to describe complex systems from epidemiology and systems biology. In this article, we report the computational requirements, the novel input model language, the available numerical solvers, and the output file format for CMS. In addition, the CMS code repository also includes a library of example model files, unit and regression tests, and documentation. Two examples, one from systems biology and the other from computational epidemiology, are included that highlight the functionality of CMS. We believe the creation of computational frameworks such as CMS will advance our scientific understanding of complex systems as well as encourage collaborative efforts for code development and knowledge sharing.

SYSTEMS & CONTROL LETTERS

This paper is concerned with the design of an augmented state feedback controller for finite-dimensional linear systems with nonlinear observation dynamics. Most of the theoretical results in the area of (optimal) feedback design are based on the assumption that the state is available for measurement. In this paper, we focus on finding a feedback control that avoids state trajectories with undesirable observability properties. In particular, we introduce an optimal control problem that specifically considers an index of observability in the control synthesis. The resulting cost functional is a combination of LQR-like quadratic terms and an index of observability. The main contribution of the paper is presenting a control synthesis procedure that on one hand, provides closed loop asymptotic stability, and addresses the observability of the system – as a transient performance criterion – on the other.

MALARIA JOURNAL

Background

While bed nets and insecticide spraying have had significant impact on malaria burden in many endemic regions, outdoor vector feeding and insecticide resistance may ultimately limit their contribution to elimination and control campaigns. Complementary vector control methods such as endectocides or systemic insecticides, where humans or animals are treated with drugs that kill mosquitoes upon ingestion via blood meal, are therefore generating much interest. This work explores the conditions under which long-lasting systemic insecticides would have a substantial impact on transmission and burden.

Methods

Hypothetical long-lasting systemic insecticides with effective durations ranging from 14 to 90 days are simulated using an individual-based mathematical model of malaria transmission. The impact of systemic insecticides when used to complement existing vector control and drug campaigns is evaluated in three settings—a highly seasonal high-transmission setting, a near-elimination setting with seasonal travel to a high-risk area, and a near-elimination setting in southern Africa.

Results

At 60% coverage, a single round of long-lasting systemic insecticide with effective duration of at least 60 days, distributed at the start of the season alongside a seasonal malaria chemoprevention campaign in a high-transmission setting, results in further burden reduction of 30–90% depending on the sub-populations targeted. In a near-elimination setting where transmission is sustained by seasonal travel to a high-risk area, targeting high-risk travellers with systemic insecticide with effective duration of at least 30 days can result in likely elimination even if intervention coverage is as low as 50%. In near-elimination settings with robust vector control, the addition of a 14-day systemic insecticide alongside an anti-malarial in mass drug administration (MDA) campaigns can decrease the necessary MDA coverage from about 85% to the more easily achievable 65%.

Conclusions

While further research into the safety profile of systemic insecticides is necessary before deployment, models predict that long-lasting systemic insecticides can play a critical role in reducing burden or eliminating malaria in a range of contexts with different target populations, existing malaria control methods, and transmission intensities. Continued investment in lengthening the duration of systemic insecticides and improving their safety profile is needed for this intervention to achieve its fullest potential.

VACCINE

Background

Measles causes significant childhood morbidity in Nigeria. Routine immunization (RI) coverage is around 40% country-wide, with very high levels of spatial heterogeneity (3–86%), with supplemental immunization activities (SIAs) at 2-year or 3-year intervals. We investigated cost savings and burden reduction that could be achieved by adjusting the inter-campaign interval by region.

Methods

We modeled 81 scenarios; permuting SIA calendars of every one, two, or three years in each of four regions of Nigeria (North-west, North-central, North-east, and South). We used an agent-based disease transmission model to estimate the number of measles cases and ingredients-based cost models to estimate RI and SIA costs for each scenario over a 10 year period.

Results

Decreasing SIAs to every three years in the North-central and South (regions of above national-average RI coverage) while increasing to every year in either the North-east or North-west (regions of below national-average RI coverage) would avert measles cases (0.4 or 1.4 million, respectively), and save vaccination costs (save $19.4 or $5.4 million, respectively), compared to a base-case of national SIAs every two years. Decreasing SIA frequency to every three years in the South while increasing to every year in the just the North-west, or in all Northern regions would prevent more cases (2.1 or 5.0 million, respectively), but would increase vaccination costs (add $3.5 million or $34.6 million, respectively), for $1.65 or $6.99 per case averted, respectively.

Conclusions

Our modeling shows how increasing SIA frequency in Northern regions, where RI is low and birth rates are high, while decreasing frequency in the South of Nigeria would reduce the number of measles cases with relatively little or no increase in vaccination costs. A national vaccination strategy that incorporates regional SIA targeting in contexts with a high level of sub-national variation would lead to improved health outcomes and/or lower costs.

VACCINE

Measles vaccination is a cost-effective way to prevent infection and reduce mortality and morbidity. However, in countries with fragile routine immunization infrastructure, coverage rates are still low and supplementary immunization campaigns (SIAs) are used to reach previously unvaccinated children. During campaigns, vaccine is generally administered to every child, regardless of their vaccination status and as a result, there is the possibility that a child that is already immune to measles (i.e. who has had 2+ vaccinations) would receive an unnecessary dose, resulting in excess cost. Selective vaccination has been proposed as one solution to this; children who were able to provide documentation of previous vaccination would not be vaccinated repeatedly. While this would result in reduced vaccine and supply cost, it would also require additional staff time and increased social mobilization investment, potentially outweighing the benefits. We utilize Monte Carlo simulation to assess under what conditions a selective vaccination policy would indeed result in net savings. We demonstrate that cost savings are possible in contexts with a high joint probability of an individual child having both 2+ previous measles doses and also an available record. We also find that the magnitude of net cost savings is highly dependent on whether a country is using measles-only or measles-rubella vaccine and on the required skill set of the individual who would review the previous vaccination records.

PLOS NTDS

Background

Pediatric diarrhea can be caused by a wide variety of pathogens, from bacteria to viruses to protozoa. Pathogen prevalence is often described as seasonal, peaking annually and associated with specific weather conditions. Although many studies have described the seasonality of diarrheal disease, these studies have occurred predominantly in temperate regions. In tropical and resource-constrained settings, where nearly all diarrhea-associated mortality occurs, the seasonality of many diarrheal pathogens has not been well characterized. As a retrospective study, we analyze the seasonal prevalence of diarrheal pathogens among children with moderate-to-severe diarrhea (MSD) over three years from the seven sites of the Global Enteric Multicenter Study (GEMS), a case–control study. Using data from this expansive study on diarrheal disease, we characterize the seasonality of different pathogens, their association with site-specific weather patterns, and consistency across study sites.

 

Methodology/Principal findings

Using traditional methodologies from signal processing, we found that certain pathogens peaked at the same time every year, but not at all sites. We also found associations between pathogen prevalence and weather or “seasons,” which are defined by applying modern machine-learning methodologies to site-specific weather data. In general, rotavirus was most prevalent during the drier “winter” months and out of phase with bacterial pathogens, which peaked during hotter and rainier times of year corresponding to “monsoon,” “rainy,” or “summer” seasons.

 

Conclusions/Significance

Identifying the seasonally-dependent prevalence for diarrheal pathogens helps characterize the local epidemiology and inform the clinical diagnosis of symptomatic children. Our multi-site, multi-continent study indicates a complex epidemiology of pathogens that does not reveal an easy generalization that is consistent across all sites. Instead, our study indicates the necessity of local data to characterizing the epidemiology of diarrheal disease. Recognition of the local associations between weather conditions and pathogen prevalence suggests transmission pathways and could inform control strategies in these settings.

Emmanuel P. Mwanga, Salum A. Mapua, Doreen J. Siria, Halfan S. Ngowo, Francis Nangacha, Joseph Mgando, Francesco Baldini, Mario González Jiménez, Heather M. Ferguson, Klaas Wynne, Prashanth Selvaraj, Simon A. Babayan & Fredros O. Okumu

MALARIA JOURNAL

Background

The propensity of different Anopheles mosquitoes to bite humans instead of other vertebrates influences their capacity to transmit pathogens to humans. Unfortunately, determining proportions of mosquitoes that have fed on humans, i.e. Human Blood Index (HBI), currently requires expensive and time-consuming laboratory procedures involving enzyme-linked immunosorbent assays (ELISA) or polymerase chain reactions (PCR). Here, mid-infrared (MIR) spectroscopy and supervised machine learning are used to accurately distinguish between vertebrate blood meals in guts of malaria mosquitoes, without any molecular techniques.

Methods

Laboratory-reared Anopheles arabiensis females were fed on humans, chickens, goats or bovines, then held for 6 to 8 h, after which they were killed and preserved in silica. The sample size was 2000 mosquitoes (500 per host species). Five individuals of each host species were enrolled to ensure genotype variability, and 100 mosquitoes fed on each. Dried mosquito abdomens were individually scanned using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra (4000 cm−1 to 400 cm−1). The spectral data were cleaned to compensate atmospheric water and CO2 interference bands using Bruker-OPUS software, then transferred to Python™ for supervised machine-learning to predict host species. Seven classification algorithms were trained using 90% of the spectra through several combinations of 75–25% data splits. The best performing model was used to predict identities of the remaining 10% validation spectra, which had not been used for model training or testing.

Results

The logistic regression (LR) model achieved the highest accuracy, correctly predicting true vertebrate blood meal sources with overall accuracy of 98.4%. The model correctly identified 96% goat blood meals, 97% of bovine blood meals, 100% of chicken blood meals and 100% of human blood meals. Three percent of bovine blood meals were misclassified as goat, and 2% of goat blood meals misclassified as human.

Conclusion

Mid-infrared spectroscopy coupled with supervised machine learning can accurately identify multiple vertebrate blood meals in malaria vectors, thus potentially enabling rapid assessment of mosquito blood-feeding histories and vectorial capacities. The technique is cost-effective, fast, simple, and requires no reagents other than desiccants. However, scaling it up will require field validation of the findings and boosting relevant technical capacity in affected countries.

Carol Camlin, Adam Akullian, Torsten Neilands, Monica Getahun, Anna Bershteyn, Sarah Ssali, ElvinGeng, Monica Gandhi, Craig Cohen, Irene Maeri,  Patrick Eyul, Maya L.Petersen, Diane Havlir, Moses Kamya, Elizabeth Bukusi, Edwin Charlebois

HEALTH & PLACE

Mobility in sub-Saharan Africa links geographically-separate HIV epidemics, intensifies transmission by enabling higher-risk sexual behavior, and disrupts care. This population-based observational cohort study measured complex dimensions of mobility in rural Uganda and Kenya. Survey data were collected every 6 months beginning in 2016 from a random sample of 2308 adults in 12 communities across three regions, stratified by intervention arm, baseline residential stability and HIV status. Analyses were survey-weighted and stratified by sex, region, and HIV status. In this study, there were large differences in the forms and magnitude of mobility across regions, between men and women, and by HIV status.

We found that adult migration varied widely by region, higher proportions of men than women migrated within the past one and five years, and men predominated across all but the most localized scales of migration: a higher proportion of women than men migrated within county of origin. Labor-related mobility was more common among men than women, while women were more likely to travel for non-labor reasons. Labor-related mobility was associated with HIV positive status for both men and women, adjusting for age and region, but the association was especially pronounced in women. The forms, drivers, and correlates of mobility in eastern Africa are complex and highly gendered. An in-depth understanding of mobility may help improve implementation and address gaps in the HIV prevention and care continua.