Reducing the Carbon Footprint of Academic Conferences:The Example of the American Society of Tropical Medicine and Hygiene

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Changes in contributions of different Anopheles vector species to malaria transmission in east and southern Africa from 2000 to 2022

Background: Malaria transmission in Africa is facilitated by multiple species of Anopheles mosquitoes. These vectors have different behaviors and vectorial capacities and are affected differently by vector control interventions, such as insecticide-treated nets and indoor residual spraying. This review aimed to assess changes in the contribution of different vector species to malaria transmission in east and southern Africa over 20 years of widespread insecticide-based vector control. Methods: We searched PubMed, Global Health, and Web of Science online databases for articles published between January 2000 and April 2023 that provided species-specific sporozoite rates for different malaria vectors in east and southern Africa. We extracted data on study characteristics, biting rates, sporozoite infection proportions, and entomological inoculation rates (EIR). Using EIR data, the proportional contribution of each species to malaria transmission was estimated. Results: Studies conducted between 2000 and 2010 identified the Anopheles gambiae complex as the primary malaria vector, while studies conducted from 2011 to 2021 indicated the dominance of Anopheles funestus. From 2000 to 2010, in 57% of sites, An. gambiae demonstrated higher parasite infection prevalence than other Anopheles species. Anopheles gambiae also accounted for over 50% of EIR in 76% of the study sites. Conversely, from 2011 to 2021, An. funestus dominated with higher infection rates than other Anopheles in 58% of sites and a majority EIR contribution in 63% of sites. This trend coincided with a decline in overall EIR and the proportion of sporozoite-infected An. gambiae. The main vectors in the An. gambiae complex in the region were Anopheles arabiensis and An. gambiae sensu stricto (s.s.), while the important member of the An. funestus group was An. funestus s.s. Conclusion: The contribution of different vector species in malaria transmission has changed over the past 20 years. As the role of An. gambiae has declined, An. funestus now appears to be dominant in most settings in east and southern Africa. Other secondary vector species may play minor roles in specific localities. To improve malaria control in the region, vector control should be optimized to match these entomological trends, considering the different ecologies and behaviors of the dominant vector species

Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis

Background: The propensity of diferent Anopheles mosquitoes to bite humans instead of other vertebrates infuences 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, midinfrared (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 refection-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 classifcation 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 identifed 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 misclassifed as goat, and 2% of goat blood meals misclassifed 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-efective, 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

Influence of larval growth and habitat shading on retreatment frequencies of biolarvicides against malaria vectors

Effective larviciding for malaria control requires detailed studies of larvicide efficacies, aquatic habitat characteristics, and life history traits of target vectors. Mosquitoes with brief larval phases present narrower timeframes for biolarvicidal effects than mosquitoes with extended periods. We evaluated two biolarvicides, VectoBac (Bacillus thuringiensis israelensis (Bti)) and VectoMax (Bti and Bacillus sphaericus) against Anopheles funestus and Anopheles arabiensis in shaded and unshaded habitats; and explored how larval development might influence retreatment intervals. These tests were done in semi-natural habitats using field-collected larvae, with untreated habitats as controls. Additionally, larval development was assessed in semi-natural and natural habitats in rural Tanzania, by sampling daily and recording larval developmental stages. Both biolarvicides reduced larval densities of both species by >98% within 72 h. Efficacy lasted one week in sun-exposed habitats but remained >50% for two weeks in shaded habitats. An. funestus spent up to two weeks before pupating (13.2(10.4–16.0) days in semi-natural; 10.0(6.6–13.5) in natural habitats), while An. arabiensis required slightly over one week (8.2 (5.8–10.6) days in semi-natural; 8.3 (5.0–11.6) in natural habitats). The findings suggest that weekly larviciding, which is essential for An.arabiensis might be more effective for An. funestus whose prolonged aquatic growth allows for repeated exposures. Additionally, the longer residual effect of biolarvicides in shaded habitats indicates they may require less frequent treatments compared to sun-exposed areas

Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis

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

Optogenetic stimulation, and control or promotion of epileptiform activity, in a mean field model of the human cortex

Epilepsy is a neural network disorder that manifests when certain elements (neuron types, sub-networks) malfunction or fail. Epileptiform activity, in turn, is characterized by the excessive synchronous firing of populations of neurons. In this dissertation, we present a mathematical model of the human cortex based on the activity of populations of neurons best captured at the meso-scale to study epileptic seizure dynamics. We then investigate further developments of the use of a spatially, temporally and cell type specific stimulation technique called optogenetics to trigger or to inhibit epileptiform activity in the cortical model.Optogenetics involves the genetic modification of a host neuron to express light activated ion channels. To incorporate this method of stimulation into the meso-scale cortical model, we first develop a scale free mathematical model of optogenetic channel dynamics, which enables study of micro-scale level optogenetic activity at the meso-scale. We then integrate the optogenetic model into the meso-scale cortical model to study the combined dynamics of cortico-optogenetic activity in time and two spatial dimensions. Through this combined model, we explore the efficacy of optogenetic stimulation in an open loop configuration to inhibit epileptic seizures. Next, we close the loop using techniques of classical control theory, and investigate the controllability of seizures in two parameter spaces that correspond well with patient seizure data. By basing our control effort on measurements of cortical activity that are clinically relevant, we aim to provide a physiologically safe and efficient way of seizure inhibition. We also study the dynamics of the combined cortico-optogenetic model using bifurcation analysis. We then explore the use of optogenetic stimulation as an excitatory technique to drive seizure like activity in a normally functioning cortical model. All of this makes a strong case for the consideration of optogenetics as a highly specific cortical stimulation modality in seizure research. Finally, we present preliminary results from work that describes the link between cortical metabolic demands and cortical activity. A quantitative definition of this link will aid in reconciling the different temporal scales of electrode measurements and imaging techniques like functional magnetic resonance imaging, while also providing a clearer picture of the role of glucose and oxygen metabolism in multiple states of cortical activity, such as normal sleep, awake, and seizure states

Vector genetics, insecticide resistance and gene drives: An agent-based modeling approach to evaluate malaria transmission and elimination.

Vector control has been a key component in the fight against malaria for decades, and chemical insecticides are critical to the success of vector control programs worldwide. However, increasing resistance to insecticides threatens to undermine these efforts. Understanding the evolution and propagation of resistance is thus imperative to mitigating loss of intervention effectiveness. Additionally, accelerated research and development of new tools that can be deployed alongside existing vector control strategies is key to eradicating malaria in the near future. Methods such as gene drives that aim to genetically modify large mosquito populations in the wild to either render them refractory to malaria or impair their reproduction may prove invaluable tools. Mathematical models of gene flow in populations, which is the transfer of genetic information from one population to another through migration, can offer invaluable insight into the behavior and potential impact of gene drives as well as the spread of insecticide resistance in the wild. Here, we present the first multi-locus, agent-based model of vector genetics that accounts for mutations and a many-to-many mapping cardinality of genotypes to phenotypes to investigate gene flow, and the propagation of gene drives in Anopheline populations. This model is embedded within a large scale individual-based model of malaria transmission representative of a high burden, high transmission setting characteristic of the Sahel. Results are presented for the selection of insecticide-resistant vectors and the spread of resistance through repeated deployment of insecticide treated nets (ITNs), in addition to scenarios where gene drives act in concert with existing vector control tools such as ITNs. The roles of seasonality, spatial distribution of vector habitat and feed sites, and existing vector control in propagating alleles that confer phenotypic traits via gene drives that result in reduced transmission are explored. The ability to model a spectrum of vector species with different genotypes and phenotypes in the context of malaria transmission allows us to test deployment strategies for existing interventions that reduce the deleterious effects of resistance and allows exploration of the impact of new tools being proposed or developed