Recasting the theory of mosquito-borne pathogen transmission dynamics and control

Microclimate factors related to dengue virus burden clusters in two endemic towns of Mexico

In dengue-endemic areas, transmission control is limited by the difficulty of achieving sufficient coverage and sustainability of interventions. To maximize the effectiveness of interventions, areas with higher transmission could be identified and prioritized. The aim was to identify burden clusters of Dengue virus (DENV) infection and evaluate their association with microclimatic factors in two endemic towns from southern Mexico. Information from a prospective population cohort study (2·5 years of follow-up) was used, microclimatic variables were calculated from satellite information, and a cross-sectional design was conducted to evaluate the relationship between the outcome and microclimatic variables in the five surveys. Spatial clustering was observed in specific geographic areas at different periods. Both, land surface temperature (aPR 0·945; IC95% 0·895-0·996) and soil humidity (aPR 3·018; IC95% 1·013-8·994), were independently associated with DENV burden clusters. These findings can help health authorities design focused dengue surveillance and control activities in dengue endemic areas.

Arbovirus impact on mosquito behavior: the jury is still out

Parasites can manipulate host behavior to enhance transmission, but our understanding of arbovirus-induced changes in mosquito behavior is limited. Here, we explore current knowledge on such behavioral alterations in mosquito vectors, focusing on host-seeking and blood-feeding behaviors. Reviewing studies on dengue, Zika, La Crosse, Sindbis, and West Nile viruses in Aedes or Culex mosquitoes reveals subtle yet potentially significant effects. However, assay heterogeneity and limited sample sizes challenge definitive conclusions. To enhance robustness, we propose using deep-learning tools for automated behavior quantification and stress the need for standardized assays. Additionally, conducting longitudinal studies across the extrinsic incubation period and integrating diverse traits into modeling frameworks are crucial for understanding the nuanced implications of arbovirus-induced behavioral changes for virus transmission dynamics.

An analytically tractable, age-structured model of the impact of vector control on mosquito-transmitted infections

Vector control is a vital tool utilised by malaria control and elimination programmes worldwide, and as such it is important that we can accurately quantify the expected public health impact of these methods. There are very few previous models that consider vector-control-induced changes in the age-structure of the vector population and the resulting impact on transmission. We analytically derive the steady-state solution of a novel age-structured deterministic compartmental model describing the mosquito feeding cycle, with mosquito age represented discretely by parity-the number of cycles (or successful bloodmeals) completed. Our key model output comprises an explicit, analytically tractable solution that can be used to directly quantify key transmission statistics, such as the effective reproductive ratio under control, Rc, and investigate the age-structured impact of vector control. Application of this model reinforces current knowledge that adult-acting interventions, such as indoor residual spraying of insecticides (IRS) or long-lasting insecticidal nets (LLINs), can be highly effective at reducing transmission, due to the dual effects of repelling and killing mosquitoes. We also demonstrate how larval measures can be implemented in addition to adult-acting measures to reduce Rc and mitigate the impact of waning insecticidal efficacy, as well as how mid-ranges of LLIN coverage are likely to experience the largest effect of reduced net integrity on transmission. We conclude that whilst well-maintained adult-acting vector control measures are substantially more effective than larval-based interventions, incorporating larval control in existing LLIN or IRS programmes could substantially reduce transmission and help mitigate any waning effects of adult-acting measures.

Evolutionary modelling indicates that mosquito metabolism shapes the life-history strategies of Plasmodium parasites

Within-host survival and between-host transmission are key life-history traits of single-celled malaria parasites. Understanding the evolutionary forces that shape these traits is crucial to predict malaria epidemiology, drug resistance, and virulence. However, very little is known about how Plasmodium parasites adapt to their mosquito vectors. Here, we examine the evolution of the time Plasmodium parasites require to develop within the vector (extrinsic incubation period) with an individual-based model of malaria transmission that includes mosquito metabolism. Specifically, we model the metabolic cascade of resource allocation induced by blood-feeding, as well as the influence of multiple blood meals on parasite development. Our model predicts that successful vector-to-human transmission events are rare, and are caused by long-lived mosquitoes. Importantly, our results show that the life-history strategies of malaria parasites depend on the mosquito's metabolic status. In our model, additional resources provided by multiple blood meals lead to selection for parasites with slow or intermediate developmental time. These results challenge the current assumption that evolution favors fast developing parasites to maximize their chances to complete their within-mosquito life cycle. We propose that the long sporogonic cycle observed for Plasmodium is not a constraint but rather an adaptation to increase transmission potential.

Targeted indoor residual insecticide applications shift Aedes aegypti age structure and arbovirus transmission potential

While residual insecticide applications have the potential to decrease pathogen transmission by reducing the density of vectors and shifting the age structure of the adult mosquito population towards younger stages of development, this double entomological impact has not been documented for Aedes aegypti. Aedes collected from households enrolled in a cluster-randomized trial evaluating the epidemiological impact of targeted indoor residual spraying (TIRS) in Merida, Mexico, were dissected and their age structure characterized by the Polovodova combined with Christopher's ovariole growth methods. In total, 813 females were dissected to characterize age structure at 1, 3, 6, and 9 months post-TIRS. Significant differences in the proportion of nulliparous Ae. aegypti females between the treatment groups was found at one-month post-TIRS (control: 35% vs. intervention: 59%), three months (20% vs. 49%) but not at six or nine months post-TIRS. TIRS significantly shiftted Ae. aegypti age structure towards younger stages and led to a non-linear reduction in survivorship compared to the control arm. Reduced survivorship also reduced the number of arbovirus transmitting females (those who survived the extrinsic incubation period). Our findings provide strong evidence of the full entomological impact of TIRS, with important implications for quantifying the epidemiological impact of vector control methods.

Bioecological Traits of Spittlebugs and Their Implications for the Epidemiology and Control of the Xylella fastidiosa Epidemic in Apulia (Southern Italy)

Spatial-temporal dynamics of spittlebug populations, together with transmission biology, are of major importance to outline the disease epidemiology of Xylella fastidiosa subsp. pauca in Apulian olive groves. The spread rate of X. fastidiosa is mainly influenced by (i) the pathogen colonization of the host plant; (ii) the acquisition of the pathogen by the vector from an infected plant, and its inoculation to healthy plants; (iii) the vector population dynamics and abundance at different spatial scales; and (iv) the dispersal of the vector. In this contribution we summarize the recent advances in research on insect vectors' traits-points ii, iii, and iv-focusing on those most relevant to X. fastidiosa epidemic in Apulia. Among the vectors' bioecological traits influencing the X. fastidiosa epidemic in olive trees, we emphasize the following: natural infectivity and transmission efficiency, phenological timing of both nymphal and adult stage, the role of seminatural vegetation as a vector reservoir in the agroecosystem and landscape, and preferential and directional dispersal capabilities. Despite the research on X. fastidiosa vectors carried out in Europe in the last decade, key uncertainties on insect vectors remain, hampering a thorough understanding of pathogen epidemiology and the development of effective and targeted management strategies. Our goal is to provide a structured and contextualized review of knowledge on X. fastidiosa vectors' key traits in the Apulian epidemic, highlighting information gaps and stimulating novel research pathways on X. fastidiosa pathosystems in Europe. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

The Importance of Including Non-Household Environments in Dengue Vector Control Activities

Most vector control activities in urban areas are focused on household environments; however, information relating to infection risks in spaces other than households is poor, and the relative risk that these spaces represent has not yet been fully understood. We used data-driven simulations to investigate the importance of household and non-household environments for dengue entomological risk in two Kenyan cities where dengue circulation has been reported. Fieldwork was performed using four strategies that targeted different stages of mosquitoes: ovitraps, larval collections, Prokopack aspiration, and BG-sentinel traps. Data were analyzed separately between household and non-household environments to assess mosquito presence, the number of vectors collected, and the risk factors for vector presence. With these data, we simulated vector and human populations to estimate the parameter m and mosquito-to-human density in both household and non-household environments. Among the analyzed variables, the main difference was found in mosquito abundance, which was consistently higher in non-household environments in Kisumu but was similar in Ukunda. Risk factor analysis suggests that small, clean water-related containers serve as mosquito breeding places in households as opposed to the trash- and rainfall-related containers found in non-household structures. We found that the density of vectors (m) was higher in non-household than household environments in Kisumu and was also similar or slightly lower between both environments in Ukunda. These results suggest that because vectors are abundant, there is a potential risk of transmission in non-household environments; hence, vector control activities should take these spaces into account.

Spatial dynamics of malaria transmission

The Ross-Macdonald model has exerted enormous influence over the study of malaria transmission dynamics and control, but it lacked features to describe parasite dispersal, travel, and other important aspects of heterogeneous transmission. Here, we present a patch-based differential equation modeling framework that extends the Ross-Macdonald model with sufficient skill and complexity to support planning, monitoring and evaluation for Plasmodium falciparum malaria control. We designed a generic interface for building structured, spatial models of malaria transmission based on a new algorithm for mosquito blood feeding. We developed new algorithms to simulate adult mosquito demography, dispersal, and egg laying in response to resource availability. The core dynamical components describing mosquito ecology and malaria transmission were decomposed, redesigned and reassembled into a modular framework. Structural elements in the framework-human population strata, patches, and aquatic habitats-interact through a flexible design that facilitates construction of ensembles of models with scalable complexity to support robust analytics for malaria policy and adaptive malaria control. We propose updated definitions for the human biting rate and entomological inoculation rates. We present new formulas to describe parasite dispersal and spatial dynamics under steady state conditions, including the human biting rates, parasite dispersal, the "vectorial capacity matrix," a human transmitting capacity distribution matrix, and threshold conditions. An [Formula: see text] package that implements the framework, solves the differential equations, and computes spatial metrics for models developed in this framework has been developed. Development of the model and metrics have focused on malaria, but since the framework is modular, the same ideas and software can be applied to other mosquito-borne pathogen systems.

Inapparent infections shape the transmission heterogeneity of dengue

Transmission heterogeneity, whereby a disproportionate fraction of pathogen transmission events result from a small number of individuals or geographic locations, is an inherent property of many, if not most, infectious disease systems. For vector-borne diseases, transmission heterogeneity is inferred from the distribution of the number of vectors per host, which could lead to significant bias in situations where vector abundance and transmission risk at the household do not correlate, as is the case with dengue virus (DENV). We used data from a contact tracing study to quantify the distribution of DENV acute infections within human activity spaces (AS), the collection of residential locations an individual routinely visits, and quantified measures of virus transmission heterogeneity from two consecutive dengue outbreaks (DENV-4 and DENV-2) that occurred in the city of Iquitos, Peru. Negative-binomial distributions and Pareto fractions showed evidence of strong overdispersion in the number of DENV infections by AS and identified super-spreading units (SSUs): i.e. AS where most infections occurred. Approximately 8% of AS were identified as SSUs, contributing to more than 50% of DENV infections. SSU occurrence was associated more with DENV-2 infection than with DENV-4, a predominance of inapparent infections (74% of all infections), households with high Aedes aegypti mosquito abundance, and high host susceptibility to the circulating DENV serotype. Marked heterogeneity in dengue case distribution, and the role of inapparent infections in defining it, highlight major challenges faced by reactive interventions if those transmission units contributing the most to transmission are not identified, prioritized, and effectively treated.

Understanding work-related travel and its relation to malaria occurrence in Thailand using geospatial maximum entropy modelling

BACKGROUND

Estimating malaria risk associated with work locations and travel across a region provides local health officials with information useful to mitigate possible transmission paths of malaria as well as understand the risk of exposure for local populations. This study investigates malaria exposure risk by analysing the spatial pattern of malaria cases (primarily Plasmodium vivax) in Ubon Ratchathani and Sisaket provinces of Thailand, using an ecological niche model and machine learning to estimate the species distribution of P. vivax malaria and compare the resulting niche areas with occupation type, work locations, and work-related travel routes.

METHODS

A maximum entropy model was trained to estimate the distribution of P. vivax malaria for a period between January 2019 and April 2020, capturing estimated malaria occurrence for these provinces. A random simulation workflow was developed to make region-based case data usable for the machine learning approach. This workflow was used to generate a probability surface for the ecological niche regions. The resulting niche regions were analysed by occupation type, home and work locations, and work-related travel routes to determine the relationship between these variables and malaria occurrence. A one-way analysis of variance (ANOVA) test was used to understand the relationship between predicted malaria occurrence and occupation type.

RESULTS

The MaxEnt (full name) model indicated a higher occurrence of P. vivax malaria in forested areas especially along the Thailand-Cambodia border. The ANOVA results showed a statistically significant difference between average malaria risk values predicted from the ecological niche model for rubber plantation workers and farmers, the two main occupation groups in the study. The rubber plantation workers were found to be at higher risk of exposure to malaria than farmers in Ubon Ratchathani and Sisaket provinces of Thailand.

CONCLUSION

The results from this study point to occupation-related factors such as work location and the routes travelled to work, being risk factors in malaria occurrence and possible contributors to transmission among local populations.

Understanding Spatiotemporal Human Mobility Patterns for Malaria Control Using a Multiagent Mobility Simulation Model

BACKGROUND

More details about human movement patterns are needed to evaluate relationships between daily travel and malaria risk at finer scales. A multiagent mobility simulation model was built to simulate the movements of villagers between home and their workplaces in 2 townships in Myanmar.

METHODS

An agent-based model (ABM) was built to simulate daily travel to and from work based on responses to a travel survey. Key elements for the ABM were land cover, travel time, travel mode, occupation, malaria prevalence, and a detailed road network. Most visited network segments for different occupations and for malaria-positive cases were extracted and compared. Data from a separate survey were used to validate the simulation.

RESULTS

Mobility characteristics for different occupation groups showed that while certain patterns were shared among some groups, there were also patterns that were unique to an occupation group. Forest workers were estimated to be the most mobile occupation group, and also had the highest potential malaria exposure associated with their daily travel in Ann Township. In Singu Township, forest workers were not the most mobile group; however, they were estimated to visit regions that had higher prevalence of malaria infection over other occupation groups.

CONCLUSIONS

Using an ABM to simulate daily travel generated mobility patterns for different occupation groups. These spatial patterns varied by occupation. Our simulation identified occupations at a higher risk of being exposed to malaria and where these exposures were more likely to occur.

Which trap is best? Alternatives to outdoor human landing catches for malaria vector surveillance: a meta-analysis

BACKGROUND

Human landing catches (HLC) are an entomological collection technique in which humans are used as attractants to capture medically relevant host-seeking mosquitoes. The use of this method has been a topic of extensive debate for decades mainly due to ethical concerns. Many alternatives to HLC have been proposed; however, no quantitative review and meta-analysis comparing HLC to outdoor alternative trapping methods has been conducted.

METHODS

A total of 58 comparisons across 12 countries were identified. We conducted a meta-analysis comparing the standardized mean difference of Anopheles captured by HLC and alternative traps. To explain heterogeneity, three moderators were chosen for analysis: trap type, location of study, and species captured. A meta-regression was fit to understand how the linear combination of moderators helped in explaining heterogeneity. The possibility of biased results due to publication bias was also explored.

RESULTS

Random-effects meta-analysis showed no statistically significant difference in the mean difference of Anopheles collected. Moderator analysis was conducted to determine the effects of trap type, geographical location of study, and the species of Anopheles captured. On average, tent-based traps captured significantly more Anopheles than outdoor HLC (95% CI: [- .9065, - 0.0544]), alternative traps in Africa captured on average more mosquitoes than outdoor HLC (95% CI: [- 2.8750, - 0.0294]), and alternative traps overall captured significantly more Anopheles gambiae s.l. than outdoor HLC (95% CI: [- 4.4613, - 0.2473]) on average. Meta-regression showed that up to 55.77% of the total heterogeneity found can be explained by a linear combination of the three moderators and the interaction between trap type and species. Subset analysis on An. gambiae s.l. showed that light traps specifically captured on average more of this species than HLC (95% CI: [- 18.3751, - 1.0629]). Publication bias likely exists. With 59.65% of studies reporting p-values less than 0.025, we believe there is an over representation in the literature of results indicating that alternative traps are superior to outdoor HLC.

CONCLUSIONS

Currently, there is no consensus on a single "magic bullet" alternative to outdoor HLC. The diversity of many alternative trap comparisons restricts potential metrics for comparisons to outdoor HLC. Further standardization and specific question-driven trap evaluations that consider target vector species and the vector control landscape are needed to allow for robust meta-analyses with less heterogeneity and to develop data-driven decision-making tools for malaria vector surveillance and control.

Factors related to human-vector contact that modify the likelihood of malaria transmission during a contained Plasmodium falciparum outbreak in Praia, Cabo Verde

Background

Determining the reproductive rate and how it varies over time and space (RT) provides important insight to understand transmission of a given disease and inform optimal strategies for controlling or eliminating it. Estimating RT for malaria is difficult partly due to the widespread use of interventions and immunity to disease masking incident infections. A malaria outbreak in Praia, Cabo Verde in 2017 provided a unique opportunity to estimate RT directly, providing a proxy for the intensity of vector-human contact and measure the impact of vector control measures.

Methods

Out of 442 confirmed malaria cases reported in 2017 in Praia, 321 (73%) were geolocated and informed this analysis. RT was calculated using the joint likelihood of transmission between two cases, based on the time (serial interval) and physical distance (spatial interval) between them. Log-linear regression was used to estimate factors associated with changes in RT, including the impact of vector control interventions. A geostatistical model was developed to highlight areas receptive to transmission where vector control activities could be focused in future to prevent or interrupt transmission.

Results

The RT from individual cases ranged between 0 and 11 with a median serial- and spatial-interval of 34 days [interquartile range (IQR): 17-52] and 1,347 m (IQR: 832-1,985 m), respectively. The number of households receiving indoor residual spraying (IRS) 4 weeks prior was associated with a reduction in RT by 0.84 [95% confidence interval (CI) 0.80-0.89; p-value <0.001] in the peak-and post-epidemic compared to the pre-epidemic period.

Conclusions

Identifying the effect of reduced human-vector contact through IRS is essential to determining optimal intervention strategies that modify the likelihood of malaria transmission and can inform optimal intervention strategies to accelerate time to elimination. The distance within which two cases are plausibly linked is important for the potential scale of any reactive interventions as well as classifying infections as imported or introduced and confirming malaria elimination.

An algebraic framework for structured epidemic modelling

Pandemic management requires that scientists rapidly formulate and analyse epidemiological models in order to forecast the spread of disease and the effects of mitigation strategies. Scientists must modify existing models and create novel ones in light of new biological data and policy changes such as social distancing and vaccination. Traditional scientific modelling workflows detach the structure of a model-its submodels and their interactions-from its implementation in software. Consequently, incorporating local changes to model components may require global edits to the code base through a manual, time-intensive and error-prone process. We propose a compositional modelling framework that uses high-level algebraic structures to capture domain-specific scientific knowledge and bridge the gap between how scientists think about models and the code that implements them. These algebraic structures, grounded in applied category theory, simplify and expedite modelling tasks such as model specification, stratification, analysis and calibration. With their structure made explicit, models also become easier to communicate, criticize and refine in light of stakeholder feedback. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

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.

Model-Based Projection of Zika Infection Risk with Temperature Effect: A Case Study in Southeast Asia

Zika virus (ZIKV) recently reemerged in the Americas and rapidly expanded in global range. It is posing significant concerns of public health due to its link to birth defects and its complicated transmission routes. Southeast Asia is badly hit by ZIKV, but limited information was found on the transmission potential of ZIKV in the region. In this paper, we develop a new dynamic process-based mathematical model, which incorporates the interactions among humans (sexual transmissibility), and between human and mosquitoes (biting transmissibility), as well as the essential impacts of temperature. The model is first validated by fitting the 2016 ZIKV outbreak in Singapore via Markov chain Monte Carlo method. Based on that, we demonstrate the effects of temperature on mosquito ecology and ZIKV transmission, and further clarify the potential risk of ZIKV outbreak in Southeast Asian countries. The results show that (i) the estimated infection reproduction number [Formula: see text] in Singapore fell from 6.93 (in which the contribution of sexual transmission was 0.89) to 0.24 after the deployment of control strategies; (ii) the optimal temperature for the reproduction of ZIKV infections and adult mosquitoes are estimated to be [Formula: see text]C and [Formula: see text]C, respectively; and (iii) the [Formula: see text] in Southeast Asia could be between 3 and 7, with an inverted-U shape around the year. The large values of [Formula: see text] and the simulative patterns of ZIKV transmission in each country highlights the high risk of ZIKV attack in Southeast Asia.

Quantifying the effect of human population mobility on malaria risk in the Peruvian Amazon

The impact of human population movement (HPM) on the epidemiology of vector-borne diseases, such as malaria, has been described. However, there are limited data on the use of new technologies for the study of HPM in endemic areas with difficult access such as the Amazon. In this study conducted in rural Peruvian Amazon, we used self-reported travel surveys and GPS trackers coupled with a Bayesian spatial model to quantify the role of HPM on malaria risk. By using a densely sampled population cohort, this study highlighted the elevated malaria transmission in a riverine community of the Peruvian Amazon. We also found that the high connectivity between Amazon communities for reasons such as work, trading or family plausibly sustains such transmission levels. Finally, by using multiple human mobility metrics including GPS trackers, and adapted causal inference methods we identified for the first time the effect of human mobility patterns on malaria risk in rural Peruvian Amazon. This study provides evidence of the causal effect of HPM on malaria that may help to adapt current malaria control programmes in the Amazon.

Real-time, spatial decision support to optimize malaria vector control: The case of indoor residual spraying on Bioko Island, Equatorial Guinea

Public health interventions require evidence-based decision-making to maximize impact. Spatial decision support systems (SDSS) are designed to collect, store, process and analyze data to generate knowledge and inform decisions. This paper discusses how the use of a SDSS, the Campaign Information Management System (CIMS), to support malaria control operations on Bioko Island has impacted key process indicators of indoor residual spraying (IRS): coverage, operational efficiency and productivity. We used data from the last five annual IRS rounds (2017 to 2021) to estimate these indicators. IRS coverage was calculated as the percentage of houses sprayed per unit area, represented by 100x100 m map-sectors. Optimal coverage was defined as between 80% and 85%, and under and overspraying as coverage below 80% and above 85%, respectively. Operational efficiency was defined as the fraction of map-sectors that achieved optimal coverage. Daily productivity was expressed as the number of houses sprayed per sprayer per day (h/s/d). These indicators were compared across the five rounds. Overall IRS coverage (i.e. percent of total houses sprayed against the overall denominator by round) was highest in 2017 (80.2%), yet this round showed the largest proportion of oversprayed map-sectors (36.0%). Conversely, despite producing a lower overall coverage (77.5%), the 2021 round showed the highest operational efficiency (37.7%) and the lowest proportion of oversprayed map-sectors (18.7%). In 2021, higher operational efficiency was also accompanied by marginally higher productivity. Productivity ranged from 3.3 h/s/d in 2020 to 3.9 h/s/d in 2021 (median 3.6 h/s/d). Our findings showed that the novel approach to data collection and processing proposed by the CIMS has significantly improved the operational efficiency of IRS on Bioko. High spatial granularity during planning and deployment together with closer follow-up of field teams using real-time data supported more homogeneous delivery of optimal coverage while sustaining high productivity.

A collaborative learning health system agent-based model: Computational and face validity

INTRODUCTION

Improving the healthcare system is a major public health challenge. Collaborative learning health systems (CLHS) - network organizations that allow all healthcare stakeholders to collaborate at scale - are a promising response. However, we know little about CLHS mechanisms of actions, nor how to optimize CLHS performance. Agent-based models (ABM) have been used to study a variety of complex systems. We translate the conceptual underpinnings of a CLHS to a computational model and demonstrate initial computational and face validity.

METHODS

CLHSs are organized to allow stakeholders (patients and families, clinicians, researchers) to collaborate, at scale, in the production and distribution of information, knowledge, and know-how for improvement. We build up a CLHS ABM from a population of patient- and doctor-agents, assign them characteristics, and set them into interaction, resulting in engagement, information, and knowledge to facilitate optimal treatment selection. To assess computational and face validity, we vary a single parameter - the degree to which patients influence other patients - and trace its effects on patient engagement, shared knowledge, and outcomes.

RESULTS

The CLHS ABM, developed in Python and using the open-source modeling framework Mesa, is delivered as a web application. The model is simulated on a cloud server and the user interface is a web browser using Python and Plotly Dash. Holding all other parameters steady, when patient influence increases, the overall patient population activation increases, leading to an increase in shared knowledge, and higher median patient outcomes.

CONCLUSIONS

We present the first theoretically-derived computational model of CLHSs, demonstrating initial computational and face validity. These preliminary results suggest that modeling CLHSs using an ABM is feasible and potentially valid. A well-developed and validated computational model of the health system may have profound effects on understanding mechanisms of action, potential intervention targets, and ultimately translation to improved outcomes.

Ecology and larval population dynamics of the primary malaria vector Nyssorhynchus darlingi in a high transmission setting dominated by fish farming in western Amazonian Brazil

Vale do Rio Juruá in western Acre, Brazil, is a persistent malaria transmission hotspot partly due to fish farming development that was encouraged to improve local standards of living. Fish ponds can be productive breeding sites for Amazonian malaria vector species, including Nyssorhynchus darlingi, which, combined with high human density and mobility, add to the local malaria burden.This study reports entomological profile of immature and adult Ny. darlingi at three sites in Mâncio Lima, Acre, during the rainy and dry season (February to September, 2017). From 63 fishponds, 10,859 larvae were collected, including 5,512 first-instar Anophelinae larvae and 4,927 second, third and fourth-instars, of which 8.5% (n = 420) were Ny. darlingi. This species was most abundant in not-abandoned fishponds and in the presence of emerging aquatic vegetation. Seasonal analysis of immatures in urban landscapes found no significant difference in the numbers of Ny. darlingi, corresponding to equivalent population density during the rainy to dry transition period. However, in the rural landscape, significantly higher numbers of Ny. darlingi larvae were collected in August (IRR = 5.80, p = 0.037) and September (IRR = 6.62, p = 0.023) (dry season), compared to February (rainy season), suggesting important role of fishponds for vector population maintenance during the seasonal transition in this landscape type. Adult sampling detected mainly Ny. darlingi (~93%), with similar outdoor feeding behavior, but different abundance according to landscape profile: urban site 1 showed higher peaks of human biting rate in May (46 bites/person/hour), than February (4) and September (15), while rural site 3 shows similar HBR during the same sampling period (22, 24 and 21, respectively). This study contributes to a better understanding of the larvae biology of the main malaria vector in the Vale do Rio Juruá region and, ultimately will support vector control efforts.

Malaria in Southern Venezuela: The hottest hotspot in Latin America

Malaria elimination in Latin America is becoming an elusive goal. Malaria cases reached a historical ~1 million in 2017 and 2018, with Venezuela contributing 53% and 51% of those cases, respectively. Historically, malaria incidence in southern Venezuela has accounted for most of the country's total number of cases. The efficient deployment of disease prevention measures and prediction of disease spread to new regions requires an in-depth understanding of spatial heterogeneity on malaria transmission dynamics. Herein, we characterized the spatial epidemiology of malaria in southern Venezuela from 2007 through 2017 and described the extent to which malaria distribution has changed country-wide over the recent years. We found that disease transmission was focal and more prevalent in the southeast region of southern Venezuela where two persistent hotspots of Plasmodium vivax (76%) and P. falciparum (18%) accounted for ~60% of the total number of cases. Such hotspots are linked to deforestation as a consequence of illegal gold mining activities. Incidence has increased nearly tenfold over the last decade, showing an explosive epidemic growth due to a significant lack of disease control programs. Our findings highlight the importance of spatially oriented interventions to contain the ongoing malaria epidemic in Venezuela. This work also provides baseline epidemiological data to assess cross-border malaria dynamics and advocates for innovative control efforts in the Latin American region.

Disease-driven reduction in human mobility influences human-mosquito contacts and dengue transmission dynamics

Heterogeneous exposure to mosquitoes determines an individual’s contribution to vector-borne pathogen transmission. Particularly for dengue virus (DENV), there is a major difficulty in quantifying human-vector contacts due to the unknown coupled effect of key heterogeneities. To test the hypothesis that the reduction of human out-of-home mobility due to dengue illness will significantly influence population-level dynamics and the structure of DENV transmission chains, we extended an existing modeling framework to include social structure, disease-driven mobility reductions, and heterogeneous transmissibility from different infectious groups. Compared to a baseline model, naïve to human pre-symptomatic infectiousness and disease-driven mobility changes, a model including both parameters predicted an increase of 37% in the probability of a DENV outbreak occurring; a model including mobility change alone predicted a 15.5% increase compared to the baseline model. At the individual level, models including mobility change led to a reduction of the importance of out-of-home onward transmission (R, the fraction of secondary cases predicted to be generated by an individual) by symptomatic individuals (up to -62%) at the expense of an increase in the relevance of their home (up to +40%). An individual’s positive contribution to R could be predicted by a GAM including a non-linear interaction between an individual’s biting suitability and the number of mosquitoes in their home (>10 mosquitoes and 0.6 individual attractiveness significantly increased R). We conclude that the complex fabric of social relationships and differential behavioral response to dengue illness cause the fraction of symptomatic DENV infections to concentrate transmission in specific locations, whereas asymptomatic carriers (including individuals in their pre-symptomatic period) move the virus throughout the landscape. Our findings point to the difficulty of focusing vector control interventions reactively on the home of symptomatic individuals, as this approach will fail to contain virus propagation by visitors to their house and asymptomatic carriers.

Human mobility patterns can play an integral role in vector-borne disease dynamics by characterizing an individual’s potential contacts with disease-transmitting vectors. Dengue virus is transmitted by a sedentary vector, but human mobility allows individuals to have contact with mosquitoes at their home and other houses they frequent (their activity space). When accounting for the decreased mobility of symptomatic dengue cases in an agent-based simulation model, however, we found a severely diminished role of the activity space in onward transmission. Those who received the majority of their mosquito contacts outside their home experienced decreases in expected bites and onward transmission when mobility changes were accounted for. Onward transmission was driven by a synergistic relationship between the number of mosquitoes in an individual’s home and their biting suitability, where even those with the highest biting suitability would have limited contribution to transmission given a low number of household mosquitoes. Reactive vector control, which often targets symptomatic cases, could be effective for slowing onward transmission from these cases, but will fail to control virus transmission due to the disproportionate contribution of asymptomatic infections.

The effectiveness of malaria camps as part of the Durgama Anchalare Malaria Nirakaran (DAMaN) program in Odisha, India: study protocol for a cluster-assigned quasi-experimental study

The Indian state of Odisha has a longstanding battle with forest malaria. Many remote and rural villages have poor access to health care, a problem that is exacerbated during the rainy season when malaria transmission is at its peak. Approximately 62% of the rural population consists of tribal groups who are among the communities most negatively impacted by malaria. To address the persistently high rates of malaria in these remote regions, the Odisha State Malaria Control Program introduced 'malaria camps' in 2017 where teams of health workers visit villages to educate the population, enhance vector control methods, and perform village-wide screening and treatment. Malaria rates declined statewide, particularly in forested areas, following the introduction of the malaria camps, but the impact of the intervention is yet to be externally evaluated. This study protocol describes a cluster-assigned quasi-experimental stepped-wedge study with a pretest-posttest control group design that evaluates if malaria camps reduce the prevalence of malaria, compared to control villages which receive the usual malaria control interventions (e.g. IRS, ITNs), as detected by PCR.

Modeling the association between Aedes aegypti ovitrap egg counts, multi-scale remotely sensed environmental data and arboviral cases at Puntarenas, Costa Rica (2017–2018)

Problems with vector surveillance are a major barrier for the effective control of vector-borne disease transmission through Latin America. Here, we present results from a 80-week longitudinal study where Aedes aegypti (L.) (Diptera: Culicidae) ovitraps were monitored weekly at 92 locations in Puntarenas, a coastal city in Costa Rica with syndemic Zika, chikungunya and dengue transmission. We used separate models to investigate the association of either Ae. aegypti-borne arboviral cases or Ae. aegypti egg counts with remotely sensed environmental variables. We also evaluated whether Ae. aegypti-borne arboviral cases were associated with Ae. aegypti egg counts. Using cross-correlation and time series modeling, we found that arboviral cases were not significantly associated with Ae. aegypti egg counts. Through model selection we found that cases had a non-linear response to multi-scale (1-km and 30-m resolution) measurements of temperature standard deviation (SD) with a lag of up to 4 weeks, while simultaneously increasing with finely-grained NDVI (30-m resolution). Meanwhile, median ovitrap Ae. aegypti egg counts increased, and respectively decreased, with temperature SD (1-km resolution) and EVI (30-m resolution) with a lag of 6 weeks. A synchrony analysis showed that egg counts had a travelling wave pattern, with synchrony showing cyclic changes with distance, a pattern not observed in remotely sensed data with 30-m and 10-m resolution. Spatially, using generalized additive models, we found that eggs were more abundant at locations with higher temperatures and where EVI was leptokurtic during the study period. Our results suggest that, in Puntarenas, remotely sensed environmental variables are associated with both Ae. aegypti-borne arbovirus transmission and Ae. aegypti egg counts from ovitraps.

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We sampled Ae. aegypti eggs using ovitraps for 80 weeks in Puntarenas, Costa Rica.

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We were able to correlate Ae. aegypti egg-counts and arboviral cases with remotely sensed data.

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Egg counts and arboviral cases were correlated with temperature and vegetation indices.

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Arboviral cases were not associated with egg counts.

Understanding how temperature shifts could impact infectious disease

Climate change is expected to have complex effects on infectious diseases, causing some to increase, others to decrease, and many to shift their distributions. There have been several important advances in understanding the role of climate and climate change on wildlife and human infectious disease dynamics over the past several years. This essay examines 3 major areas of advancement, which include improvements to mechanistic disease models, investigations into the importance of climate variability to disease dynamics, and understanding the consequences of thermal mismatches between host and parasites. Applying the new information derived from these advances to climate-disease models and addressing the pressing knowledge gaps that we identify should improve the capacity to predict how climate change will affect disease risk for both wildlife and humans.

Deriving risk maps from epidemiological models of vector borne diseases: State-of-the-art and suggestions for best practice

Epidemiological models (EMs) are widely used to predict the temporal outbreak risk of vector-borne diseases (VBDs). EMs typically use the basic reproduction number (R₀), a threshold quantity, to indicate risk. To provide an overall view of the risk, these model outputs can be transformed into spatial risk maps, using various aggregation methods (e.g. average R₀ over time, cumulative number of days with R₀ > 1). However, there is no standardized methodology available for this. Depending on the specific aggregation methods used, the yielded spatial risk maps may have considerably different interpretations. Additionally, the method used to visualize the aggregated data also affects the perceived spatial patterns. In this review, we compare commonly used aggregation and visualization methods and discuss the respective interpretation of risk maps. Research publications using epidemiological modelling methods were drawn from Web of Science. Only publications containing maps of R₀ transformed from EMs were considered for the analysis. An example EM was applied to illustrate how aggregation and visualization methods affect the final presentations of risk maps. Risk maps can be generated to show duration, intensity and spatio-temporal dynamics of potential outbreak risk of VBDs. We show that 1) different temporal aggregation methods lead to different interpretations; 2) similar spatial patterns do not necessarily bear the same meaning; 3) visualization methods considerably affect how results are perceived, and thus should be applied with caution. We recommend mapping both intensity and duration of the VBD outbreak risk, using small time-steps to show spatio-temporal dynamics when possible.

Effects of Mosquito Biology on Modeled Chikungunya Virus Invasion Potential in Florida

Arboviruses transmitted by Aedes aegypti and Aedes albopictus have been introduced to Florida on many occasions. Infrequently, these introductions lead to sporadic local transmission and, more rarely, sustained local transmission. Both mosquito species are present in Florida, with spatio-temporal variation in population composition. We developed a two-vector compartmental, deterministic model to investigate factors influencing Chikungunya virus (CHIKV) establishment. The model includes a nonlinear, temperature-dependent mosquito mortality function based on minimum mortality in a central temperature region. Latin Hypercube sampling was used to generate parameter sets used to simulate transmission dynamics, following the introduction of one infected human. The analysis was repeated for three values of the mortality function central temperature. Mean annual temperature was consistently important in the likelihood of epidemics, and epidemics increased as the central temperature increased. Ae. albopictus recruitment was influential at the lowest central temperature while Ae. aegypti recruitment was influential at higher central temperatures. Our results indicate that the likelihood of CHIKV establishment may vary, but overall Florida is permissive for introductions. Model outcomes were sensitive to the specifics of mosquito mortality. Mosquito biology parameters are variable, and improved understanding of this variation will improve our ability to predict the outcome of introductions.

Inference and prediction of malaria transmission dynamics using time series data

BACKGROUND

Disease surveillance systems are essential for effective disease intervention and control by monitoring disease prevalence as time series. To evaluate the severity of an epidemic, statistical methods are widely used to forecast the trend, seasonality, and the possible number of infections of a disease. However, most statistical methods are limited in revealing the underlying dynamics of disease transmission, which may be affected by various impact factors, such as environmental, meteorological, and physiological factors. In this study, we focus on investigating malaria transmission dynamics based on time series data.

METHODS

A data-driven nonlinear stochastic model is proposed to infer and predict the dynamics of malaria transmission based on the time series of prevalence data. Specifically, the dynamics of malaria transmission is modeled based on the notion of vectorial capacity (VCAP) and entomological inoculation rate (EIR). A particle Markov chain Monte Carlo (PMCMC) method is employed to estimate the model parameters. Accordingly, a one-step-ahead prediction method is proposed to project the number of future malaria infections. Finally, two case studies are carried out on the inference and prediction of Plasmodium vivax transmission in Tengchong and Longling, Yunnan province, China.

RESULTS

The results show that the trained data-driven stochastic model can well fit the historical time series of P. vivax prevalence data in both counties from 2007 to 2010. Moreover, with well-trained model parameters, the proposed one-step-ahead prediction method can achieve better performances than that of the seasonal autoregressive integrated moving average model with respect to predicting the number of future malaria infections.

CONCLUSIONS

By involving dynamically changing impact factors, the proposed data-driven model together with the PMCMC method can successfully (i) depict the dynamics of malaria transmission, and (ii) achieve accurate one-step-ahead prediction about malaria infections. Such a data-driven method has the potential to investigate malaria transmission dynamics in other malaria-endemic countries/regions.

The Role of Vector Trait Variation in Vector-Borne Disease Dynamics

Many important endemic and emerging diseases are transmitted by vectors that are biting arthropods. The functional traits of vectors can affect pathogen transmission rates directly and also through their effect on vector population dynamics. Increasing empirical evidence shows that vector traits vary significantly across individuals, populations, and environmental conditions, and at time scales relevant to disease transmission dynamics. Here, we review empirical evidence for variation in vector traits and how this trait variation is currently incorporated into mathematical models of vector-borne disease transmission. We argue that mechanistically incorporating trait variation into these models, by explicitly capturing its effects on vector fitness and abundance, can improve the reliability of their predictions in a changing world. We provide a conceptual framework for incorporating trait variation into vector-borne disease transmission models, and highlight key empirical and theoretical challenges. This framework provides a means to conceptualize how traits can be incorporated in vector borne disease systems, and identifies key areas in which trait variation can be explored. Determining when and to what extent it is important to incorporate trait variation into vector borne disease models remains an important, outstanding question.

When Is a Plasmodium-Infected Mosquito an Infectious Mosquito?

Plasmodium parasites experience significant bottlenecks as they transit through the mosquito and are transmitted to their mammalian host. Oocyst prevalence on mosquito midguts and sporozoite prevalence in salivary glands are nevertheless commonly used to confirm successful malaria transmission, assuming that these are reliable indicators of the mosquito's capacity to give rise to secondary infections. Here we discuss recent insights in sporogonic development and transmission bottlenecks for Plasmodium. We highlight critical gaps in our knowledge and frame their importance in understanding the human and mosquito reservoirs of infection. A better understanding of the events that lead to successful inoculation of infectious sporozoites by mosquitoes is critical to designing effective interventions to shrink the malaria map.

Malaria Elimination in Costa Rica: Changes in Treatment and Mass Drug Administration

Costa Rica is a candidate to eliminate malaria by 2020. The remaining malaria transmission hotspots are located within the Huétar Norte Region (HNR), where 90% of the country's 147 malaria cases have occurred since 2016, following a 33-month period without transmission. Here, we examine changes in transmission with the implementation of a supervised seven-day chloroquine and primaquine treatment (7DCPT). We also evaluate the impact of a focal mass drug administration (MDA) in January 2019 at Boca Arenal, the town in HNR reporting the greatest local transmission. We found that the change to a seven-day treatment protocol, from the prior five-day program, was associated with a 98% reduction in malaria transmission. The MDA helped to reduce transmission, keeping the basic reproduction number, R_T, significantly below 1, for at least four months. However, following new imported cases from Nicaragua, autochthonous transmission resumed. Our results highlight the importance of appropriate treatment delivery to reduce malaria transmission, and the challenge that highly mobile populations, if their malaria is not treated, pose to regional elimination efforts in Mesoamerica and México.

Health policy impacts on malaria transmission in Costa Rica

Costa Rica is near malaria elimination. This achievement has followed shifts in malaria health policy. Here, we evaluate the impacts that different health policies have had on malaria transmission in Costa Rica from 1913 to 2018. We identified regime shifts and used regression models to measure the impact of different health policies on malaria transmission in Costa Rica using annual case records. We found that vector control and prophylactic treatments were associated with a 50% malaria case reduction in 1929-1931 compared with 1913-1928. DDT introduction in 1946 was associated with an increase in annual malaria case reduction from 7.6% (1942-1946) to 26.4% (1947-1952). The 2006 introduction of 7-day supervised chloroquine and primaquine treatments was the most effective health policy between 1957 and 2018, reducing annual malaria cases by 98% (2009-2018) when compared with 1957-1968. We also found that effective malaria reduction policies have been sensitive to natural catastrophes and extreme climatic events, both of which have increased malaria transmission in Costa Rica. Currently, outbreaks follow malaria importation into vulnerable areas of Costa Rica. This highlights the need to timely diagnose and treat malaria, while improving living standards, in the affected areas.

Protecting the peri-domestic environment: the challenge for eliminating residual malaria

Malaria transmission after universal access and use of malaria preventive services is known as residual malaria transmission. The concurrent spatial-temporal distributions of people and biting mosquitoes in malaria endemic villages determines where and when residual malaria transmission occurs. Understanding human and vector population behaviors and movements is a critical first step to prevent mosquito bites to eliminate residual malaria transmission. This study identified where people in the Solomon Islands are over 24-hour periods. Participants (59%) were predominantly around the house but not in their house when most biting by Anopheles farauti, the dominant malaria vector, occurs. While 84% of people slept under a long-lasting insecticide-treated bed net (LLIN), on average only 7% were under an LLIN during the 18:00 to 21:00 h peak mosquito biting period. On average, 34% of participants spend at least one night away from their homes each fortnight. Despite high LLIN use while sleeping, most human biting by An. farauti occurs early in the evening before people go to sleep when people are in peri-domestic areas (predominantly on verandas or in kitchen areas). Novel vector control tools that protect individuals from mosquito bites between sundown and when people sleep are needed for peri-domestic areas.

Effectiveness of an intervention for Aedes aegypti control scaled-up under an inter-sectoral approach in a Colombian city hyper-endemic for dengue virus

Aedes aegypti transmitted arboviral diseases are of significant importance in Colombia, particularly since the 2014/2015 introduction of chikungunya and Zika in the Americas and the increasing spread of dengue. In response, the Colombian government initiated the scaling-up of a community-based intervention under inter and multi-sector partnerships in two out of four sectors in Girardot, one of the most hyper-endemic dengue cities in the country. Using a quasi-experimental research design a scaled-up community-led Aedes control intervention was assessed for its capacity to reduce dengue from January 2010 to August 2017 in Girardot, Colombia. Reported dengue cases, and associated factors were analysed from available data sets from the Colombian disease surveillance systems. We estimated the reduction in dengue cases before and after the intervention using, Propensity Score Matching and an Autoregressive Moving Average model for robustness. In addition, the differences in dengue incidence among scaling-up phases (pre-implementation vs sustainability) and between treatment groups (intervention and control areas) were modelled. Evidence was found in favour of the intervention, although to maximise impact the scaling-up of the intervention should continue until it covers the remaining sectors. It is expected that a greater impact of the intervention can be documented in the next outbreak of dengue in Girardot.

Risk Factors for Household Vector Abundance Using Indoor CDC Light Traps in a High Malaria Transmission Area of Northern Zambia

Malaria transmission is dependent on the density and distribution of mosquito vectors, but drivers of vector abundance have not been adequately studied across a range of transmission settings. To inform intervention strategies for high-burden areas, further investigation is needed to identify predictors of vector abundance. Active household (HH) surveillance was conducted in Nchelenge district, Luapula Province, northern Zambia, a high-transmission setting with limited impact of malaria control. Between April 2012 and July 2017, mosquitoes were collected indoors during HH visits using CDC light traps. Demographic, environmental, and climatological correlates of vector abundance were identified using log-binomial regression models with robust standard errors. The primary malaria vectors in this setting were Anopheles funestus sensu stricto (s.s.) and Anopheles gambiae s.s. Anopheles funestus predominated in both seasons, with a peak in the dry season. Anopheles gambiae peaked at lower numbers in the rainy season. Environmental, climatic, and demographic factors were correlated with HH vector abundance. Higher vector counts were found in rural areas with low population density and among HHs close to roads and small streams. Vector counts were lower with increasing elevation and slope. Anopheles funestus was negatively associated with rainfall at lags of 2-6 weeks, and An. gambiae was positively associated with rainfall at lags of 3-10 weeks. Both vectors had varying relationships with temperature. These results suggest that malaria vector control in Nchelenge district should occur throughout the year, with an increased focus on dry-season transmission and rural areas.

The basic reproductive number for disease systems with multiple coupled heterogeneities

In mathematical epidemiology, a well-known formula describes the impact of heterogeneity on the basic reproductive number, R₀, for situations in which transmission is separable and for which there is one source of variation in susceptibility and one source of variation in infectiousness. This formula is written in terms of the magnitudes of the heterogeneities, as quantified by their coefficients of variation, and the correlation between them. A natural question to ask is whether analogous results apply when there are multiple sources of variation in susceptibility and/or infectiousness. In this paper we demonstrate that with three or more coupled heterogeneities, R₀ under separable transmission depends on details of the distribution of the heterogeneities in a way that is not seen in the well-known simpler situation. We provide explicit formulae for the cases of multivariate normal and multivariate log-normal distributions, showing that R₀ can again be expressed in terms of the magnitudes of the heterogeneities and the pairwise correlations between them. The formulae, however, differ between the two multivariate distributions, demonstrating that no formula of this type applies generally when there are three or more coupled heterogeneities. We see that the results of the formulae are approximately equal when heterogeneities are relatively small and show that an earlier result in the literature (Koella, 1991) should be viewed in this light. We provide numerical illustrations of our results and discuss a setting in which coupled heterogeneities are likely to have a major impact on the value of R₀. We also describe a rather surprising result: in a system with three heterogeneities, R₀ can exhibit non-monotonic behavior with increasing levels of heterogeneity, in marked contrast to the familiar two heterogeneity setting in which R₀ either increases or decreases with increasing heterogeneity.

Dengue illness impacts daily human mobility patterns in Iquitos, Peru

Background

Human mobility plays a central role in shaping pathogen transmission by generating spatial and/or individual variability in potential pathogen-transmitting contacts. Recent research has shown that symptomatic infection can influence human mobility and pathogen transmission dynamics. Better understanding the complex relationship between symptom severity, infectiousness, and human mobility requires quantification of movement patterns throughout infectiousness. For dengue virus (DENV), human infectiousness peaks 0–2 days after symptom onset, making it paramount to understand human movement patterns from the beginning of illness.

Methodology and principal findings

Through community-based febrile surveillance and RT-PCR assays, we identified a cohort of DENV+ residents of the city of Iquitos, Peru (n = 63). Using retrospective interviews, we measured the movements of these individuals when healthy and during each day of symptomatic illness. The most dramatic changes in mobility occurred during the first three days after symptom onset; individuals visited significantly fewer locations (Wilcoxon test, p = 0.017) and spent significantly more time at home (Wilcoxon test, p = 0.005), compared to when healthy. By 7–9 days after symptom onset, mobility measures had returned to healthy levels. Throughout an individual’s symptomatic period, the day of illness and their subjective sense of well-being were the most significant predictors for the number of locations and houses they visited.

Conclusions/Significance

Our study is one of the first to collect and analyze human mobility data at a daily scale during symptomatic infection. Accounting for the observed changes in human mobility throughout illness will improve understanding of the impact of disease on DENV transmission dynamics and the interpretation of public health-based surveillance data.

Dengue is the most important mosquito-borne viral disease of humans worldwide. Due to the limited mobility of the mosquitoes that transmit dengue virus, human mobility can be a key to both understanding an individual’s exposure to the virus and explaining the spread of dengue throughout a population. Accurate disease models should include human mobility; however, changes in human movement patterns due to the presence of symptoms need to be taken into account. We quantified the impact of symptom presence on human mobility throughout the infectious period by analyzing a dataset on the daily movements of dengue virus infected individuals. Accounting for these changing patterns of mobility will improve understanding of the complex relationship between symptom severity, human movement, and dengue virus transmission. Furthermore, dengue transmission models that incorporate symptom-driven mobility changes can be used to evaluate scenarios and strategies for disease prevention.

The use of GPS data loggers to describe the impact of spatio-temporal movement patterns on malaria control in a high-transmission area of northern Zambia

Background

Human movement is a driver of malaria transmission and has implications for sustainable malaria control. However, little research has been done on the impact of fine-scale movement on malaria transmission and control in high-transmission settings. As interest in targeted malaria control increases, evaluations are needed to determine the appropriateness of these strategies in the context of human mobility across a variety of transmission settings.

Methods

A human mobility study was conducted in Nchelenge District, a high-transmission setting in northern Zambia. Over 1 year, 84 participants were recruited from active malaria surveillance cohorts to wear a global positioning system data logger for 1 month during all daily activity. Participants completed a survey questionnaire and underwent malaria testing and treatment at the time of logger distribution and at collection 1 month later. Incident malaria infections were identified using polymerase chain reaction. Participant movement was characterized throughout the study area and across areas targeted for an indoor residual spraying (IRS) intervention. Participant movement patterns were compared using movement intensity maps, activity space plots, and statistical analyses. Malaria risk was characterized across participants using spatial risk maps and time spent away from home during peak vector biting hours.

Results

Movement data were collected from 82 participants, and 63 completed a second study visit. Participants exhibited diverse mobility patterns across the study area, including movement into and out of areas targeted for IRS, potentially mitigating the impact of IRS on parasite prevalence. Movement patterns did not differ significantly by season or age, but male participants traveled longer distances and spent more time away from home. Monthly malaria incidence was 22%, and malaria risk was characterized as high across participants. Participants with incident parasitemia traveled a shorter distance and spent more time away from home during peak biting hours; however, these relationships were not statistically significant, and malaria risk score did not differ by incident parasitemia.

Conclusions

Individual movement patterns in Nchelenge District, Zambia have implications for malaria control, particularly the effectiveness of targeted IRS strategies. Large and fine-scale population mobility patterns should be considered when planning intervention strategies across transmission settings.

Electronic supplementary material

The online version of this article (10.1186/s12942-019-0183-y) contains supplementary material, which is available to authorized users.

Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data

Time series data provide a crucial window into infectious disease dynamics, yet their utility is often limited by the spatially aggregated form in which they are presented. When working with time series data, violating the implicit assumption of homogeneous dynamics below the scale of spatial aggregation could bias inferences about underlying processes. We tested this assumption in the context of the 2015-2016 Zika epidemic in Colombia, where time series of weekly case reports were available at national, departmental, and municipal scales. First, we performed a descriptive analysis, which showed that the timing of departmental-level epidemic peaks varied by three months and that departmental-level estimates of the time-varying reproduction number, R(t), showed patterns that were distinct from a national-level estimate. Second, we applied a classification algorithm to six features of proportional cumulative incidence curves, which showed that variability in epidemic duration, the length of the epidemic tail, and consistency with a cumulative normal density curve made the greatest contributions to distinguishing groups. Third, we applied this classification algorithm to data simulated with a stochastic transmission model, which showed that group assignments were consistent with simulated differences in the basic reproduction number, R₀. This result, along with associations between spatial drivers of transmission and group assignments based on observed data, suggests that the classification algorithm is capable of detecting differences in temporal patterns that are associated with differences in underlying drivers of incidence patterns. Overall, this diversity of temporal patterns at local scales underscores the value of spatially disaggregated time series data.

Climate Change Impacts on Waterborne Diseases: Moving Toward Designing Interventions

PURPOSE

Climate change threatens progress achieved in global reductions of infectious disease rates over recent decades. This review summarizes literature on potential impacts of climate change on waterborne diseases, organized around a framework of questions that can be addressed depending on available data.

RECENT FINDINGS

A growing body of evidence suggests that climate change may alter the incidence of waterborne diseases, and diarrheal diseases in particular. Much of the existing work examines historical relationships between weather and diarrhea incidence, with a limited number of studies projecting future disease rates. Some studies take social and ecological factors into account in considerations of historical relationships, but few have done so in projecting future conditions. The field is at a point of transition, toward incorporating social and ecological factors into understanding the relationships between climatic factors and diarrheal diseases and using this information for future projections. The integration of these components helps identify vulnerable populations and prioritize adaptation strategies.

Sex-Specific Asymmetrical Attack Rates in Combined Sexual-Vectorial Transmission Epidemics

In 2015–2016, South America went through the largest Zika epidemic in recorded history. One important aspect of this epidemic was the importance of sexual transmission in combination with the usual vectorial transmission, with asymmetrical transmissibilities between sexual partners depending on the type of sexual contact; this asymmetry manifested itself in data as an increased risk to women. We propose a mathematical model for the transmission of the Zika virus including sexual transmission via all forms of sexual contact, as well as vector transmission, assuming a constant availability of mosquitoes. From this model, we derive an expression for R0, which is used to study and analyze the relative contributions of the male to female sexual transmission route vis-à-vis vectorial transmission. We also perform Bayesian inference of the model’s parameters using data from the 2016 Zika epidemic in Rio de Janeiro.

Vector competence, vectorial capacity of Nyssorhynchus darlingi and the basic reproduction number of Plasmodium vivax in agricultural settlements in the Amazonian Region of Brazil

Background

Brazilian malaria control programmes successfully reduced the incidence and mortality rates from 2005 to 2016. Since 2017, increased malaria has been reported across the Amazon. Few field studies focus on the primary malaria vector in high to moderate endemic areas, Nyssorhynchus darlingi, as the key entomological component of malaria risk, and on the metrics of Plasmodium vivax propagation in Amazonian rural communities.

Methods

Human landing catch collections were carried out in 36 houses of 26 communities in five municipalities in the Brazilian states of Acre, Amazonas and Rondônia states, with API (> 30). In addition, data on the number of locally acquired symptomatic infections were employed in mathematical modelling analyses carried out to determine Ny. darlingi vector competence and vectorial capacity to P. vivax; and to calculate the basic reproduction number for P. vivax.

Results

Entomological indices and malaria metrics ranged among localities: prevalence of P. vivax infection in Ny. darlingi, from 0.243% in Mâncio Lima, Acre to 3.96% in Machadinho D’Oeste, Rondônia; daily human-biting rate per person from 23 ± 1.18 in Cruzeiro do Sul, Acre, to 66 ± 2.41 in Lábrea, Amazonas; vector competence from 0.00456 in São Gabriel da Cachoeira, Amazonas to 0.04764 in Mâncio Lima, Acre; vectorial capacity from 0.0836 in Mâncio Lima, to 1.5 in Machadinho D’Oeste. The estimated R₀ for P. vivax (PvR₀) was 3.3 in Mâncio Lima, 7.0 in Lábrea, 16.8 in Cruzeiro do Sul, 55.5 in São Gabriel da Cachoeira, and 58.7 in Machadinho D’Oeste. Correlation between P. vivax prevalence in Ny. darlingi and vector competence was non-linear whereas association between prevalence of P. vivax in mosquitoes, vectorial capacity and R₀ was linear and positive.

Conclusions

In spite of low vector competence of Ny. darlingi to P. vivax, parasite propagation in the human population is enhanced by the high human-biting rate, and relatively high vectorial capacity. The high PvR₀ values suggest hyperendemicity in Machadinho D’Oeste and São Gabriel da Cachoeira at levels similar to those found for P. falciparum in sub-Saharan Africa regions. Mass screening for parasite reservoirs, effective anti-malarial drugs and vector control interventions will be necessary to shrinking transmission in Amazonian rural communities, Brazil.

Electronic supplementary material

The online version of this article (10.1186/s12936-019-2753-7) contains supplementary material, which is available to authorized users.

Estimation of DENV-2 Transmission as a Function of Site-Specific Entomological Parameters from Three Cities in Colombia

Background:

Measuring dengue virus transmission in endemic areas is a difficult task as many variables drive transmission, and often are not independent of one another.

Objectives:

We aimed to determine the utility of vectorial capacity to explain the observed dengue infection rates in three hyperendemic cities in Colombia, and tested hypotheses related to three variables: mosquito density, effective vector competence, and biting rate.

Methods:

We estimated two of the most influential entomological variables related to cumulative vectorial capacity, which is a modification of the traditional vectorial capacity equation, of three Colombian mosquito populations. Laboratory studies were undertaken to measure vector competence and man biting rate of local mosquito populations. In addition, the assessment of cumulative vectorial capacity also incorporated site-specific estimations of mosquito density and the probability of daily survival from previous studies conducted in those cities.

Findings:

We found that the biting rates and mosquito infection rates differed among populations of mosquitoes from these three cities, resulting in differences in the site-specific measures of transmission potential. Specifically, we found that using site-specific entomological measures to populate the cumulative vectorial capacity equation was best at recapitulating observed mosquito infection rates when mosquito density was discounted compared to when we incorporated site-specific density measures.

Conclusions:

Specific mosquito-biting rate is likely sufficient to explain transmission differences in these three cities, confirming that this parameter is a critical parameter when predicting and assessing dengue transmission in three Colombian cities with different field observed transmission patterns.

Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building

Due to a combination of ecological, political, and demographic factors, the emergence of novel pathogens has been increasingly observed in animals and humans in recent decades. Enhancing global capacity to study and interpret infectious disease surveillance data, and to develop data-driven computational models to guide policy, represents one of the most cost-effective, and yet overlooked, ways to prepare for the next pandemic. Epidemiological and behavioral data from recent pandemics and historic scourges have provided rich opportunities for validation of computational models, while new sequencing technologies and the 'big data' revolution present new tools for studying the epidemiology of outbreaks in real time. For the past two decades, the Division of International Epidemiology and Population Studies (DIEPS) of the NIH Fogarty International Center has spearheaded two synergistic programs to better understand and devise control strategies for global infectious disease threats. The Multinational Influenza Seasonal Mortality Study (MISMS) has strengthened global capacity to study the epidemiology and evolutionary dynamics of influenza viruses in 80 countries by organizing international research activities and training workshops. The Research and Policy in Infectious Disease Dynamics (RAPIDD) program and its precursor activities has established a network of global experts in infectious disease modeling operating at the research-policy interface, with collaborators in 78 countries. These activities have provided evidence-based recommendations for disease control, including during large-scale outbreaks of pandemic influenza, Ebola and Zika virus. Together, these programs have coordinated international collaborative networks to advance the study of emerging disease threats and the field of computational epidemic modeling. A global community of researchers and policy-makers have used the tools and trainings developed by these programs to interpret infectious disease patterns in their countries, understand modeling concepts, and inform control policies. Here we reflect on the scientific achievements and lessons learnt from these programs (h-index = 106 for RAPIDD and 79 for MISMS), including the identification of outstanding researchers and fellows; funding flexibility for timely research workshops and working groups (particularly relative to more traditional investigator-based grant programs); emphasis on group activities such as large-scale modeling reviews, model comparisons, forecasting challenges and special journal issues; strong quality control with a light touch on outputs; and prominence of training, data-sharing, and joint publications.