Estimating the extrinsic incubation period of malaria using a mechanistic model of sporogony

Estimating the effects of temperature on transmission of the human malaria parasite, Plasmodium falciparum

Despite concern that climate change could increase the human risk to malaria in certain areas, the temperature dependency of malaria transmission is poorly characterized. Here, we use a mechanistic model fitted to experimental data to describe how Plasmodium falciparum infection of the African malaria vector, Anopheles gambiae, is modulated by temperature, including its influences on parasite establishment, conversion efficiency through parasite developmental stages, parasite development rate, and overall vector competence. We use these data, together with estimates of the survival of infected blood-fed mosquitoes, to explore the theoretical influence of temperature on transmission in four locations in Kenya, considering recent conditions and future climate change. Results provide insights into factors limiting transmission in cooler environments and indicate that increases in malaria transmission due to climate warming in areas like the Kenyan Highlands, might be less than previously predicted.

Investigating the dose-dependency of the midgut escape barrier using a mechanistic model of within-mosquito dengue virus population dynamics

Arboviruses can emerge rapidly and cause explosive epidemics of severe disease. Some of the most epidemiologically important arboviruses, including dengue virus (DENV), Zika virus (ZIKV), Chikungunya (CHIKV) and yellow fever virus (YFV), are transmitted by Aedes mosquitoes, most notably Aedes aegypti and Aedes albopictus. After a mosquito blood feeds on an infected host, virus enters the midgut and infects the midgut epithelium. The virus must then overcome a series of barriers before reaching the mosquito saliva and being transmitted to a new host. The virus must escape from the midgut (known as the midgut escape barrier; MEB), which is thought to be mediated by transient changes in the permeability of the midgut-surrounding basal lamina layer (BL) following blood feeding. Here, we present a mathematical model of the within-mosquito population dynamics of DENV (as a model system for mosquito-borne viruses more generally) that includes the interaction of the midgut and BL which can account for the MEB. Our results indicate a dose-dependency of midgut establishment of infection as well as rate of escape from the midgut: collectively, these suggest that the extrinsic incubation period (EIP)-the time taken for DENV virus to be transmissible after infection-is shortened when mosquitoes imbibe more virus. Additionally, our experimental data indicate that multiple blood feeding events, which more closely mimic mosquito-feeding behavior in the wild, can hasten the course of infections, and our model predicts that this effect is sensitive to the amount of virus imbibed. Our model indicates that mutations to the virus which impact its replication rate in the midgut could lead to even shorter EIPs when double-feeding occurs. Mechanistic models of within-vector viral infection dynamics provide a quantitative understanding of infection dynamics and could be used to evaluate novel interventions that target the mosquito stages of the infection.

Quantification of sporozoite expelling by Anopheles mosquitoes infected with laboratory and naturally circulating P. falciparum gametocytes

It is currently unknown whether all Plasmodium falciparum-infected mosquitoes are equally infectious. We assessed sporogonic development using cultured gametocytes in the Netherlands and naturally circulating strains in Burkina Faso. We quantified the number of sporozoites expelled into artificial skin in relation to intact oocysts, ruptured oocysts, and residual salivary gland sporozoites. In laboratory conditions, higher total sporozoite burden was associated with shorter duration of sporogony (p<0.001). Overall, 53% (116/216) of infected Anopheles stephensi mosquitoes expelled sporozoites into artificial skin with a median of 136 expelled sporozoites (interquartile range [IQR], 34-501). There was a strong positive correlation between ruptured oocyst number and salivary gland sporozoite load (ρ = 0.8; p<0.0001) and a weaker positive correlation between salivary gland sporozoite load and number of sporozoites expelled (ρ = 0.35; p=0.0002). In Burkina Faso, Anopheles coluzzii mosquitoes were infected by natural gametocyte carriers. Among salivary gland sporozoite positive mosquitoes, 89% (33/37) expelled sporozoites with a median of 1035 expelled sporozoites (IQR, 171-2969). Again, we observed a strong correlation between ruptured oocyst number and salivary gland sporozoite load (ρ = 0.9; p<0.0001) and a positive correlation between salivary gland sporozoite load and the number of sporozoites expelled (ρ = 0.7; p<0.0001). Several mosquitoes expelled multiple parasite clones during probing. Whilst sporozoite expelling was regularly observed from mosquitoes with low infection burdens, our findings indicate that mosquito infection burden is positively associated with the number of expelled sporozoites. Future work is required to determine the direct implications of these findings for transmission potential.

Impact of climate change on temperature variations and extrinsic incubation period of malaria parasites in Chennai, India: implications for its disease transmission potential

BACKGROUND

The global temperature has significantly risen in the past century. Studies have indicated that higher temperature intensifies malaria transmission in tropical and temperate countries. Temperature fluctuations will have a potential impact on parasite development in the vector Anopheles mosquito.

METHODS

Year-long microclimate temperatures were recorded from a malaria-endemic area, Chennai, India, from September 2021 to August 2022. HOBO data loggers were placed in different vector resting sites including indoor and outdoor roof types. Downloaded temperatures were categorised by season, and the mean temperature was compared with data from the same study area recorded from November 2012 to October 2013. The extrinsic incubation period for Plasmodium falciparum and P. vivax was calculated from longitudinal temperatures recorded during both periods. Vector surveillance was also carried out in the area during the summer season.

RESULTS

In general, temperature and daily temperature range (DTR) have increased significantly compared to the 2012-2013 data, especially the DTR of indoor asbestos structures, from 4.30 ℃ to 12.62 ℃ in 2021-2022, unlike the marginal increase observed in thatched and concrete structures. Likewise, the average DTR of outdoor asbestos structures increased from 5.02 ℃ (2012-2013) to 8.76 ℃ (2021-2022) although the increase was marginal in thatched structures and, surprisingly, showed no such changes in concrete structures. The key finding of the extrinsic incubation period (EIP) is that a decreasing trend was observed in 2021-2022 compared to 2012-2013, mainly in indoor asbestos structures from 7.01 to 6.35 days, which negatively correlated with the current observation of an increase in temperature. Vector surveillance undertaken in the summer season revealed the presence of Anopheles breeding in various habitats. Anopheles stephensi could be collected using CDC light traps along with other mosquito species.

CONCLUSION

The microclimate temperature has increased significantly over the years, and mosquitoes are gradually adapting to this rising temperature. Temperature negatively correlates with the extrinsic incubation period of the parasite. As the temperature increases, the development of the parasite in An. stephensi will be faster because of a decrease in EIP, thus requiring relatively fewer days, posing a risk for disease transmission and a hindrance to malaria elimination efforts.

Mathematical assessment of the role of intervention programs for malaria control

Malaria remains a global health problem despite the many attempts to control and eradicate it. There is an urgent need to understand the current transmission dynamics of malaria and to determine the interventions necessary to control malaria. In this paper, we seek to develop a fit-for-purpose mathematical model to assess the interventions needed to control malaria in an endemic setting. To achieve this, we formulate a malaria transmission model to analyse the spread of malaria in the presence of interventions. A sensitivity analysis of the model is performed to determine the relative impact of the model parameters on disease transmission. We explore how existing variations in the recruitment and management of intervention strategies affect malaria transmission. Results obtained from the study imply that the discontinuation of existing interventions has a significant effect on malaria prevalence. Thus, the maintenance of interventions is imperative for malaria elimination and eradication. In a scenario study aimed at assessing the impact of long-lasting insecticidal nets (LLINs), indoor residual spraying (IRS), and localized individual measures, our findings indicate that increased LLINs utilization and extended IRS coverage (with longer-lasting insecticides) cause a more pronounced reduction in symptomatic malaria prevalence compared to a reduced LLINs utilization and shorter IRS coverage. Additionally, our study demonstrates the impact of localized preventive measures in mitigating the spread of malaria when compared to the absence of interventions.

Investigating the dose-dependency of the midgut escape barrier using a mechanistic model of within-mosquito dengue virus population dynamics

Flaviviruses are arthropod-borne (arbo)viruses which can emerge rapidly and cause explosive epidemics of severe disease. Some of the most epidemiologically important flaviviruses, including dengue virus (DENV), Zika virus (ZIKV) and yellow fever virus (YFV), are transmitted by Aedes mosquitoes, most notably Aedes aegypti and Aedes albopictus. After a mosquito blood feeds on an infected host, virus enters the midgut and infects the midgut epithelium. The virus must then overcome a series of barriers before reaching the mosquito saliva and being transmitted to a new host. The virus must escape from the midgut (known as the midgut escape barrier; MEB), which is thought to be mediated by transient changes in the permeability of the midgut-surrounding basal lamina layer (BL) following blood feeding. Here, we present a mathematical model of the within-mosquito population dynamics of flaviviruses that includes the interaction of the midgut and BL which can account for the MEB. Our results indicate a dose-dependency of midgut establishment of infection as well as rate of escape from the midgut: collectively, these suggest that the extrinsic incubation period (EIP) - the time taken for DENV virus to be transmissible after infection - is shortened when mosquitoes imbibe more virus. Additionally, our experimental data indicates that multiple blood feeding events, which more closely mimic mosquito-feeding behavior in the wild, can hasten the course of infections, and our model predicts that this effect is sensitive to the amount of virus imbibed. Our model indicates that mutations to the virus which impact its replication rate in the midgut could lead to even shorter EIPs when double-feeding occurs. Mechanistic models of within-vector viral infection dynamics provide a quantitative understanding of infection dynamics and could be used to evaluate novel interventions that target the mosquito stages of the infection.

Author summary

Aedes mosquitoes are the main vectors of dengue virus (DENV), Zika virus (ZIKV) and yellow fever virus (YFV), all of which can cause severe disease in humans with dengue alone infecting an estimated 100-400 million people each year. Understanding the processes that affect whether, and at which rate, mosquitoes may transmit such viruses is, hence, paramount. Here, we present a mathematical model of virus dynamics within infected mosquitoes. By combining the model with novel experimental data, we show that the course of infection is sensitive to the initial dose of virus ingested by the mosquito. The data also indicates that mosquitoes which blood feed subsequent to becoming infected may be able to transmit infection earlier, which is reproduced in the model. This is important as many mosquito species feed multiple times during their lifespan and, any reduction in time to dissemination will increase the number of days that a mosquito is infectious and so enhance the risk of transmission. Our study highlights the key and complementary roles played by mathematical models and experimental data for understanding within-mosquito virus dynamics.

Environmental Change, Changing Biodiversity, and Infections-Lessons for Kidney Health Community

There is a direct and accelerating connection between ongoing environmental change, the unprecedented decline in biodiversity, and the increase in infectious disease epidemiology worldwide. Rising global temperatures are threatening the biodiversity that underpins the richness and diversity of flora and fauna species in our ecosystem. Anthropogenic activities such as burning fossil fuels, deforestation, rapid urbanization, and expanding population are the primary drivers of environmental change resulting in biodiversity collapse. Climate change is influencing the emergence, prevalence, and transmission of infectious diseases both directly and through its impact on biodiversity. The environment is gradually becoming more suitable for infectious diseases by affecting a variety of pathogens, hosts, and vectors and by favoring transmission rates in many parts of the world that were until recently free of these infections. The acute effects of these zoonotic, vector and waterborne diseases are well known; however, evidence is emerging about their role in the development of chronic kidney disease. The pathways linking environmental change and biodiversity loss to infections impacting kidney health are diverse and complex. Climate change and biodiversity loss disproportionately affect the vulnerable and limit their ability to access healthcare. The kidney health community needs to contribute to the issue of environmental change and biodiversity loss through multisectoral action alongside government, policymakers, advocates, businesses, and the general population. We describe various aspects of the environmental change effects on the transmission and emergence of infectious diseases particularly focusing on its potential impact on kidney health. We also discuss the adaptive and mitigation measures and the gaps in research and policy action.

Autocorrelated measurement processes and inference for ordinary differential equation models of biological systems

Ordinary differential equation models are used to describe dynamic processes across biology. To perform likelihood-based parameter inference on these models, it is necessary to specify a statistical process representing the contribution of factors not explicitly included in the mathematical model. For this, independent Gaussian noise is commonly chosen, with its use so widespread that researchers typically provide no explicit justification for this choice. This noise model assumes ‘random’ latent factors affect the system in the ephemeral fashion resulting in unsystematic deviation of observables from their modelled counterparts. However, like the deterministically modelled parts of a system, these latent factors can have persistent effects on observables. Here, we use experimental data from dynamical systems drawn from cardiac physiology and electrochemistry to demonstrate that highly persistent differences between observations and modelled quantities can occur. Considering the case when persistent noise arises owing only to measurement imperfections, we use the Fisher information matrix to quantify how uncertainty in parameter estimates is artificially reduced when erroneously assuming independent noise. We present a workflow to diagnose persistent noise from model fits and describe how to remodel accounting for correlated errors.

Application of machine and deep learning algorithms in optical microscopic detection of Plasmodium: A malaria diagnostic tool for the future

Machine and deep learning techniques are prevalent in the medical discipline due to their high level of accuracy in disease diagnosis. One such disease is malaria caused by Plasmodium falciparum and transmitted by the female anopheles mosquito. According to the World Health Organisation (WHO), millions of people are infected annually, leading to inevitable deaths in the infected population. Statistical records show that early detection of malaria parasites could prevent deaths and machine learning (ML) has proved helpful in the early detection of malarial parasites. Human error is identified to be a major cause of inaccurate diagnostics in the traditional microscopy malaria diagnosis method. Therefore, the method would be more reliable if human expert dependency is restricted or entirely removed, and thus, the motivation of this paper. This study presents a systematic review to understand the prevalent machine learning algorithms applied to a low-cost, portable optical microscope in the automation of blood film interpretation for malaria parasite detection. Peer-reviewed papers were downloaded from selected reputable databases eg. Elsevier, IEEExplore, Pubmed, Scopus, Web of Science, etc. The extant literature suggests that convolutional neural network (CNN) and its variants (deep learning) account for 41.9% of the microscopy malaria diagnosis using machine learning with a prediction accuracy of 99.23%. Thus, the findings suggest that early detection of the malaria parasite has improved through the application of CNN and other ML algorithms on microscopic malaria parasite detection.

Targeting malaria parasites inside mosquitoes: ecoevolutionary consequences

Proof-of-concept studies demonstrate that antimalarial drugs designed for human treatment can also be applied to mosquitoes to interrupt malaria transmission. Deploying a new control tool is ideally undertaken within a stewardship programme that maximises a drug's lifespan by minimising the risk of resistance evolution and slowing its spread once emerged. We ask: what are the epidemiological and evolutionary consequences of targeting parasites within mosquitoes? Our synthesis argues that targeting parasites inside mosquitoes (i) can be modelled by readily expanding existing epidemiological frameworks; (ii) provides a functionally novel control method that has potential to be more robust to resistance evolution than targeting parasites in humans; and (iii) could extend the lifespan and clinical benefit of antimalarials used exclusively to treat humans.

Modeling cellular co-infection and reassortment of bluetongue virus in Culicoides midges

When related segmented RNA viruses co-infect a single cell, viral reassortment can occur, potentially leading to new strains with pandemic potential. One virus capable of reassortment is bluetongue virus (BTV), which causes substantial health impacts in ruminants and is transmitted via Culicoides midges. Because midges can become co-infected by feeding on multiple different host species and remain infected for their entire life span, there is a high potential for reassortment to occur. Once a midge is co-infected, additional barriers must be crossed for a reassortant virus to emerge, such as cellular co-infection and dissemination of reassortant viruses to the salivary glands. We developed three mathematical models of within-midge BTV dynamics of increasing complexity, allowing us to explore the conditions leading to the emergence of reassortant viruses. In confronting the simplest model with published data, we estimate that the average life span of a bluetongue virion in the midge midgut is about 6 h, a key determinant of establishing a successful infection. Examination of the full model, which permits cellular co-infection and reassortment, shows that small differences in fitness of the two infecting strains can have a large impact on the frequency with which reassortant virions are observed. This is consistent with experimental co-infection studies with BTV strains with different relative fitnesses that did not produce reassortant progeny. Our models also highlight several gaps in existing data that would allow us to elucidate these dynamics in more detail, in particular the times it takes the virus to disseminate to different tissues, and measurements of viral load and reassortant frequency at different temperatures.

Omitting age-dependent mosquito mortality in malaria models underestimates the effectiveness of insecticide-treated nets

Mathematical models of vector-borne infections, including malaria, often assume age-independent mortality rates of vectors, despite evidence that many insects senesce. In this study we present survival data on insecticide-resistant Anopheles gambiae s.l. from experiments in Côte d’Ivoire. We fit a constant mortality function and two age-dependent functions (logistic and Gompertz) to the data from mosquitoes exposed (treated) and not exposed (control) to insecticide-treated nets (ITNs), to establish biologically realistic survival functions. This enables us to explore the effects of insecticide exposure on mosquito mortality rates, and the extent to which insecticide resistance might impact the effectiveness of ITNs. We investigate this by calculating the expected number of infectious bites a mosquito will take in its lifetime, and by extension the vectorial capacity. Our results show that the predicted vectorial capacity is substantially lower in mosquitoes exposed to ITNs, despite the mosquitoes in the experiment being highly insecticide-resistant. The more realistic age-dependent functions provide a better fit to the experimental data compared to a constant mortality function and, hence, influence the predicted impact of ITNs on malaria transmission potential. In models with age-independent mortality, there is a great reduction for the vectorial capacity under exposure compared to no exposure. However, the two age-dependent functions predicted an even larger reduction due to exposure, highlighting the impact of incorporating age in the mortality rates. These results further show that multiple exposures to ITNs had a considerable effect on the vectorial capacity. Overall, the study highlights the importance of including age dependency in mathematical models of vector-borne disease transmission and in fully understanding the impact of interventions.

Interventions against malaria are most commonly targeted on the adult mosquitoes, which transmit the infection from person to person. One of the most important interventions are bed-nets, treated with insecticides. Unfortunately, extensive exposure of mosquitoes to insecticide has led to widespread evolution of insecticide resistance, which might threaten control strategies. Piecing together the overall impact of resistance on the efficacy of insecticide-treated nets is complex, but can be informed by the use of mathematical models. However, there are some assumptions that the models frequently use which are not realistic in terms of the mosquito biology. In this paper, we formulate a model that includes age-dependent mortality rates, an important parameter in vector control since control strategies most commonly aim to reduce the lifespan of the mosquitoes. By using novel data collected using field-derived insecticide-resistant mosquitoes, we explore the effects that the presence of insecticides on nets have on the mortality rates, as well as the difference incorporating age dependency in the model has on the results. We find that including age-dependent mortality greatly alters the anticipated effects of insecticide-treated nets on mosquito transmission potential, and that ignoring this realism potentially overestimates the negative impact of insecticide resistance.

Hotspots and super-spreaders: Modelling fine-scale malaria parasite transmission using mosquito flight behaviour

Malaria hotspots have been the focus of public health managers for several years due to the potential elimination gains that can be obtained from targeting them. The identification of hotspots must be accompanied by the description of the overall network of stable and unstable hotspots of malaria, especially in medium and low transmission settings where malaria elimination is targeted. Targeting hotspots with malaria control interventions has, so far, not produced expected benefits. In this work we have employed a mechanistic-stochastic algorithm to identify clusters of super-spreader houses and their related stable hotspots by accounting for mosquito flight capabilities and the spatial configuration of malaria infections at the house level. Our results show that the number of super-spreading houses and hotspots is dependent on the spatial configuration of the villages. In addition, super-spreaders are also associated to house characteristics such as livestock and family composition. We found that most of the transmission is associated with winds between 6pm and 10pm although later hours are also important. Mixed mosquito flight (downwind and upwind both with random components) were the most likely movements causing the spread of malaria in two out of the three study areas. Finally, our algorithm (named MALSWOTS) provided an estimate of the speed of malaria infection progression from house to house which was around 200-400 meters per day, a figure coherent with mark-release-recapture studies of Anopheles dispersion. Cross validation using an out-of-sample procedure showed accurate identification of hotspots. Our findings provide a significant contribution towards the identification and development of optimal tools for efficient and effective spatio-temporal targeted malaria interventions over potential hotspot areas.

The potential impact of Anopheles stephensi establishment on the transmission of Plasmodium falciparum in Ethiopia and prospective control measures

BACKGROUND

Sub-Saharan Africa has seen substantial reductions in cases and deaths due to malaria over the past two decades. While this reduction is primarily due to an increasing expansion of interventions, urbanisation has played its part as urban areas typically experience substantially less malaria transmission than rural areas. However, this may be partially lost with the invasion and establishment of Anopheles stephensi. A. stephensi, the primary urban malaria vector in Asia, was first detected in Africa in 2012 in Djibouti and was subsequently identified in Ethiopia in 2016, and later in Sudan and Somalia. In Djibouti, malaria cases have increased 30-fold from 2012 to 2019 though the impact in the wider region remains unclear.

METHODS

Here, we have adapted an existing model of mechanistic malaria transmission to estimate the increase in vector density required to explain the trends in malaria cases seen in Djibouti. To account for the observed plasticity in An. stephensi behaviour, and the unknowns of how it will establish in a novel environment, we sample behavioural parameters in order to account for a wide range of uncertainty. This quantification is then applied to Ethiopia, considering temperature-dependent extrinsic incubation periods, pre-existing vector-control interventions and Plasmodium falciparum prevalence in order to assess the potential impact of An. stephensi establishment on P. falciparum transmission. Following this, we estimate the potential impact of scaling up ITN (insecticide-treated nets)/IRS (indoor residual spraying) and implementing piperonyl butoxide (PBO) ITNs and larval source management, as well as their economic costs.

RESULTS

We estimate that annual P. falciparum malaria cases could increase by 50% (95% CI 14-90) if no additional interventions are implemented. The implementation of sufficient control measures to reduce malaria transmission to pre-stephensi levels will cost hundreds of millions of USD.

CONCLUSIONS

Substantial heterogeneity across the country is predicted and large increases in vector control interventions could be needed to prevent a major public health emergency.

Bluetongue Research at a Crossroads: Modern Genomics Tools Can Pave the Way to New Insights

Bluetongue virus (BTV) is an arthropod-borne, segmented double-stranded RNA virus that can cause severe disease in both wild and domestic ruminants. BTV evolves via several key mechanisms, including the accumulation of mutations over time and the reassortment of genome segments.Additionally, BTV must maintain fitness in two disparate hosts, the insect vector and the ruminant. The specific features of viral adaptation in each host that permit host-switching are poorly characterized. Limited field studies and experimental work have alluded to the presence of these phenomena at work, but our understanding of the factors that drive or constrain BTV's genetic diversification remains incomplete. Current research leveraging novel approaches and whole genome sequencing applications promises to improve our understanding of BTV's evolution, ultimately contributing to the development of better predictive models and management strategies to reduce future impacts of bluetongue epizootics.