MGDrivE 3: A decoupled vector-human framework for epidemiological simulation of mosquito genetic control tools and their surveillance.

Novel mosquito genetic control tools, such as CRISPR-based gene drives, hold great promise in reducing the global burden of vector-borne diseases. As these technologies advance through the research and development pipeline, there is a growing need for modeling frameworks incorporating increasing levels of entomological and epidemiological detail in order to address questions regarding logistics and biosafety. Epidemiological predictions are becoming increasingly relevant to the development of target product profiles and the design of field trials and interventions, while entomological surveillance is becoming increasingly important to regulation and biosafety. We present MGDrivE 3 (Mosquito Gene Drive Explorer 3), a new version of a previously-developed framework, MGDrivE 2, that investigates the spatial population dynamics of mosquito genetic control systems and their epidemiological implications. The new framework incorporates three major developments: i) a decoupled sampling algorithm allowing the vector portion of the MGDrivE framework to be paired with a more detailed epidemiological framework, ii) a version of the Imperial College London malaria transmission model, which incorporates age structure, various forms of immunity, and human and vector interventions, and iii) a surveillance module that tracks mosquitoes captured by traps throughout the simulation. Example MGDrivE 3 simulations are presented demonstrating the application of the framework to a CRISPR-based homing gene drive linked to dual disease-refractory genes and their potential to interrupt local malaria transmission. Simulations are also presented demonstrating surveillance of such a system by a network of mosquito traps. MGDrivE 3 is freely available as an open-source R package on CRAN (https://cran.r-project.org/package=MGDrivE2) (version 2.1.0), and extensive examples and vignettes are provided. We intend the software to aid in understanding of human health impacts and biosafety of mosquito genetic control tools, and continue to iterate per feedback from the genetic control community.

Efficacy of a spatial repellent for control of Aedes-borne virus transmission: A cluster-randomized trial in Iquitos, Peru.

Over half the world's population is at risk for viruses transmitted by Aedes mosquitoes, such as dengue and Zika. The primary vector, Aedes aegypti, thrives in urban environments. Despite decades of effort, cases and geographic range of Aedes-borne viruses (ABVs) continue to expand. Rigorously proven vector control interventions that measure protective efficacy against ABV diseases are limited to Wolbachia in a single trial in Indonesia and do not include any chemical intervention. Spatial repellents, a new option for efficient deployment, are designed to decrease human exposure to ABVs by releasing active ingredients into the air that disrupt mosquito-human contact. A parallel, cluster-randomized controlled trial was conducted in Iquitos, Peru, to quantify the impact of a transfluthrin-based spatial repellent on human ABV infection. From 2,907 households across 26 clusters (13 per arm), 1,578 participants were assessed for seroconversion (primary endpoint) by survival analysis. Incidence of acute disease was calculated among 16,683 participants (secondary endpoint). Adult mosquito collections were conducted to compare Ae. aegypti abundance, blood-fed rate, and parity status through mixed-effect difference-in-difference analyses. The spatial repellent significantly reduced ABV infection by 34.1% (one-sided 95% CI lower limit, 6.9%; one-sided P value = 0.0236, z = 1.98). Aedes aegypti abundance and blood-fed rates were significantly reduced by 28.6 (95% CI 24.1%, ∞); z = -9.11) and 12.4% (95% CI 4.2%, ∞); z = -2.43), respectively. Our trial provides conclusive statistical evidence from an appropriately powered, preplanned cluster-randomized controlled clinical trial of the impact of a chemical intervention, in this case a spatial repellent, to reduce the risk of ABV transmission compared to a placebo.

Climate change: A driver of increasing vector-borne disease transmission in non-endemic areas.

In this Perspective, Shlomit Paz discusses the link between climate change and transmission of vector-borne diseases in non-endemic areas.

Evaluating the long-term impact of COVID-19-associated public health interventions on zoonotic and vector-borne diseases in China: an interrupted time series analysis.

BACKGROUND: The long-term impact of COVID-19-associated public health interventions on zoonotic and vector-borne infectious diseases (ZVBs) remains uncertain. This study sought to examine the changes in ZVBs in China during the COVID-19 pandemic and predict their future trends. METHODS: Monthly incidents of seven ZVBs (Hemorrhagic fever with renal syndrome [HFRS], Rabies, Dengue fever [DF], Human brucellosis [HB], Leptospirosis, Malaria, and Schistosomiasis) were gathered from January 2004 to July 2023. An autoregressive fractionally integrated moving average (ARFIMA) by incorporating the COVID-19-associated public health intervention variables was developed to evaluate the long-term effectiveness of interventions and forecast ZVBs epidemics from August 2023 to December 2025. RESULTS: Over the study period, there were 1,599,647 ZVBs incidents. HFRS and rabies exhibited declining trends, HB showed an upward trajectory, while the others remained relatively stable. The ARFIMA, incorporating a pulse pattern, estimated the average monthly number of changes of - 83 (95% confidence interval [CI] - 353-189) cases, - 3 (95% CI - 33-29) cases, - 468 (95% CI - 1531-597) cases, 2191 (95% CI 1056-3326) cases, 7 (95% CI - 24-38) cases, - 84 (95% CI - 222-55) cases, and - 214 (95% CI - 1036-608) cases for HFRS, rabies, DF, HB, leptospirosis, malaria, and schistosomiasis, respectively, although these changes were not statistically significant besides HB. ARFIMA predicted a decrease in HB cases between August 2023 and December 2025, while indicating a relative plateau for the others. CONCLUSIONS: China's dynamic zero COVID-19 strategy may have exerted a lasting influence on HFRS, rabies, DF, malaria, and schistosomiasis, beyond immediate consequences, but not affect HB and leptospirosis. ARFIMA emerges as a potent tool for intervention analysis, providing valuable insights into the sustained effectiveness of interventions. Consequently, the application of ARFIMA contributes to informed decision-making, the design of effective interventions, and advancements across various fields.

Vector-borne disease models with Lagrangian approach.

We develop a multi-group and multi-patch model to study the effects of population dispersal on the spatial spread of vector-borne diseases across a heterogeneous environment. The movement of host and/or vector is described by Lagrangian approach in which the origin or identity of each individual stays unchanged regardless of movement. The basic reproduction number [Formula: see text] of the model is defined and the strong connectivity of the host-vector network is succinctly characterized by the residence times matrices of hosts and vectors. Furthermore, the definition and criterion of the strong connectivity of general infectious disease networks are given and applied to establish the global stability of the disease-free equilibrium. The global dynamics of the model system are shown to be entirely determined by its basic reproduction number. We then obtain several biologically meaningful upper and lower bounds on the basic reproduction number which are independent or dependent of the residence times matrices. In particular, the heterogeneous mixing of hosts and vectors in a homogeneous environment always increases the basic reproduction number. There is a substantial difference on the upper bound of [Formula: see text] between Lagrangian and Eulerian modeling approaches. When only host movement between two patches is concerned, the subdivision of hosts (more host groups) can lead to a larger basic reproduction number. In addition, we numerically investigate the dependence of the basic reproduction number and the total number of infected hosts on the residence times matrix of hosts, and compare the impact of different vector control strategies on disease transmission.

A hybrid Lagrangian-Eulerian model for vector-borne diseases.

In this paper, a multi-patch and multi-group vector-borne disease model is proposed to study the effects of host commuting (Lagrangian approach) and/or vector migration (Eulerian approach) on disease spread. We first define the basic reproduction number of the model, R 0 , which completely determines the global dynamics of the model system. Namely, if R 0 ≤ 1 , then the disease-free equilibrium is globally asymptotically stable, and if R 0 > 1 , then there exists a unique endemic equilibrium which is globally asymptotically stable. Then, we show that the basic reproduction number has lower and upper bounds which are independent of the host residence times matrix and the vector migration matrix. In particular, nonhomogeneous mixing of hosts and vectors in a homogeneous environment generally increases disease persistence and the basic reproduction number of the model attains its minimum when the distributions of hosts and vectors are proportional. Moreover, R 0 can also be estimated by the basic reproduction numbers of disconnected patches if the environment is homogeneous. The optimal vector control strategy is obtained for a special scenario. In the two-patch and two-group case, we numerically analyze the dependence of the basic reproduction number and the total number of infected people on the host residence times matrix and illustrate the optimal vector control strategy in homogeneous and heterogeneous environments.

Microscopic and molecular investigation of vector borne haemoprotozoan diseases in dromedary camel of North Gujarat, India.

BACKGROUND OBJECTIVES: Vector-borne haemoprotozoan diseases comprise diverse group of single celled organism transmitted by haematophagus invertebrates. The current study was aimed at the identification of major haemoprotozoan (Babesia, Theileria and Trypanosoma) in dromedary camel of North Gujarat region in India using microscopy and Polymerase Chain Reaction (PCR). METHODS: A total of 234 blood samples were screened by the microscopic and molecular detection assays. Molecular prevalence studies of Theileria, Trypanosoma spp and Babesia was undertaken using 18s ribosomal DNA, RoTat 1.2 and SS rRNA gene respectively. The data relating to microscopic and molecular prevalence along with associated risk factors were analysed by statistical methods. RESULTS: The overall prevalence of hamoprotozoan disease based on microscopic and molecular investigation was 23.50%. The sensitivity and specificity (95% Confidence Interval) of PCR assay was 100% in comparison to microscopy (45.45 % sensitive and 100 % specific). The kappa coefficient between PCR and microscopy indicated good level of agreement with a value of 0.704 and SE of 0.159. INTERPRETATION CONCLUSION: Despite holding much significance to the animal sector, little work has been undertaken in regional parts of India regarding camel parasites. The present study offers first preliminary research data investigating haemoprotozoan disease using parasitological and molecular methods in camels in the region.

Vector-Borne Diseases Potpourri.

The Intergovernmental Panel on Climate Change has reported that the prevalence of vector-borne diseases has increased in recent decades and that the prevalence of malaria, Lyme disease, dengue, and, in particular, West Nile virus infection are expected to increase further if control measures are not strengthened. (1)(2) This review article summarizes the epidemiology, various clinical manifestations, and management strategies of these vector-borne diseases with increasing prevalence both in the United States and worldwide.

Dynamical footprints enable detection of disease emergence.

Developing methods for anticipating the emergence or reemergence of infectious diseases is both important and timely; however, traditional model-based approaches are stymied by uncertainty surrounding the underlying drivers. Here, we demonstrate an operational, mechanism-agnostic detection algorithm for disease (re-)emergence based on early warning signals (EWSs) derived from the theory of critical slowing down. Specifically, we used computer simulations to train a supervised learning algorithm to detect the dynamical footprints of (re-)emergence present in epidemiological data. Our algorithm was then challenged to forecast the slowly manifesting, spatially replicated reemergence of mumps in England in the mid-2000s and pertussis post-1980 in the United States. Our method successfully anticipated mumps reemergence 4 years in advance, during which time mitigation efforts could have been implemented. From 1980 onwards, our model identified resurgent states with increasing accuracy, leading to reliable classification starting in 1992. Additionally, we successfully applied the detection algorithm to 2 vector-transmitted case studies, namely, outbreaks of dengue serotypes in Puerto Rico and a rapidly unfolding outbreak of plague in 2017 in Madagascar. Taken together, these findings illustrate the power of theoretically informed machine learning techniques to develop early warning systems for the (re-)emergence of infectious diseases.

Building resilience to mosquito-borne diseases in the Caribbean.

Small island developing states in the Caribbean are among the most vulnerable countries on the planet to climate variability and climate change. In the last 3 decades, the Caribbean region has undergone frequent and intense heat waves, storms, floods, and droughts. This has had a detrimental impact on population health and well-being, including an increase in infectious disease outbreaks. Recent advances in climate science have enhanced our ability to anticipate hydrometeorological hazards and associated public health challenges. Here, we discuss progress towards bridging the gap between climate science and public health decision-making in the Caribbean to build health system resilience to extreme climatic events. We focus on the development of climate services to help manage mosquito-transmitted disease epidemics. There are numerous areas of ongoing biological research aimed at better understanding the direct and indirect impacts of climate change on the transmission of mosquito-borne diseases. Here, we emphasise additional factors that affect our ability to operationalise this biological understanding. We highlight a lack of financial resources, technical expertise, data sharing, and formalised partnerships between climate and health communities as major limiting factors to developing sustainable climate services for health. Recommendations include investing in integrated climate, health and mosquito surveillance systems, building regional and local human resource capacities, and designing national and regional cross-sectoral policies and national action plans. This will contribute towards achieving the Sustainable Development Goals (SDGs) and maximising regional development partnerships and co-benefits for improved health and well-being in the Caribbean.

Basic reproduction ratio of a mosquito-borne disease in heterogeneous environment.

To explore the influence of spatial heterogeneity on mosquito-borne diseases, we formulate a reaction-diffusion model with general incidence rates. The basic reproduction ratio [Formula: see text] for this model is introduced and the threshold dynamics in terms of [Formula: see text] are obtained. In the case where the model is spatially homogeneous, the global asymptotic stability of the endemic equilibrium is proved when [Formula: see text]. Under appropriate conditions, we establish the asymptotic profiles of [Formula: see text] in the case of small or large diffusion rates, and investigate the monotonicity of [Formula: see text] with respect to the heterogeneous diffusion coefficients. Numerically, the proposed model is applied to study the dengue fever transmission. Via performing simulations on the impacts of certain factors on [Formula: see text] and disease dynamics, we find some novel and interesting phenomena which can provide valuable information for the targeted implementation of disease control measures.

Threshold dynamics of an almost periodic vector-borne disease model.

Many infectious diseases cannot be transmitted from human to human directly, and the transmission needs to be done via a vector. It is well known that vectors' life cycles are highly dependent on their living environment. In order to investigate dynamics of vector-borne diseases under environment influence, we propose a vector-borne disease model with almost periodic coefficients. We derive the basic reproductive number [Formula: see text] for this model and establish a threshold type result on its global dynamics in terms of [Formula: see text]. As an illustrative example, we consider an almost periodic model of malaria transmission. Our numerical simulation results show that the basic reproductive number may be underestimated if almost periodic coefficients are replaced by their average values . Finally, we use our model to study the dengue fever transmission in Guangdong, China. The parameters are chosen to fit the reported data available for Guangdong. Numerical simulations indicate that the annual dengue fever case in Guangdong will increase steadily in the near future unless more effective control measures are implemented. Sensitivity analysis implies that the parameters with strong impact on the outcome are recovery rate, mosquito recruitment rate, mosquito mortality rate, baseline transmission rates between mosquito and human. This suggests that the effective control strategies may include intensive treatment, mosquito control, decreasing human contact number with mosquitoes (e.g., using bed nets and preventing mosquito bites), and environmental modification.

Impact of the COVID-19 pandemic on the incidence of dengue fever in Peru.

Dengue virus and severe acute respiratory syndrome coronavirus 2 coexist in dengue-endemic countries; therefore, the adoption of preventive measures is essential to control the spread of both viruses. We conducted an ecological study to compare the temporal patterns of the incidence of dengue before and during the Coronavirus disease 2019 (COVID-19) pandemic in Peru. A time-series analysis comparing the incidence of dengue using a Student's t test with variance correction was performed. Poisson regression was applied to determine the incidence rate ratio (IRR) of dengue before and during the COVID-19 pandemic. The incidence of dengue was found to be increased in all endemic regions of Peru during the COVID-19 pandemic, with the highest incidences registered in Ica (IRR = 90.14), Huánuco (IRR = 38.6), and Ucayali (IRR = 23.78), with the exception of Piura (IRR = 0.83). The highest increases in the number of dengue cases per million inhabitants were in Ucayali (393.38), Tumbes (233.19), Ica (166.08), and Loreto (129.93). The gradient of dengue cases was positive in all endemic regions during the COVID-19 pandemic. The number of dengue cases per million increased during the COVID-19 pandemic throughout Peru and in several endemic regions, with the exception of Piura.

Coinfection between SARS-CoV-2 and vector-borne diseases in Luanda, Angola.

Co-epidemics happening simultaneously can generate a burden on healthcare systems. The co-occurrence of SARS-CoV-2 with vector-borne diseases (VBD), such as malaria and dengue in resource-limited settings represents an additional challenge to the healthcare systems. Herein, we assessed the coinfection rate between SARS-CoV-2 and VBD to highlight the need to carry out an accurate diagnosis and promote timely measures for these infections in Luanda, the capital city of Angola. This was a cross-sectional study conducted with 105 subjects tested for the SARS-CoV-2 and VBD with a rapid detection test in April 2021. The participants tested positive for SARS-CoV-2 (3.80%), malaria (13.3%), and dengue (27.6%). Low odds related to testing positivity to SARS-CoV-2 or VBD were observed in participants above or equal to 40 years (odds ratio [OR]: 0.60, p = 0.536), while higher odds were observed in male (OR: 1.44, p = 0.392) and urbanized areas (OR: 3.78, p = 0.223). The overall co-infection rate between SARS-CoV-2 and VBD was 11.4%. Our findings showed a coinfection between SARS-CoV-2 with malaria and dengue, which could indicate the need to integrate the screening for VBD in the SARS-CoV-2 testing algorithm and the adjustment of treatment protocols. Further studies are warranted to better elucidate the relationship between COVID-19 and VBD in Angola.

Development, environmental degradation, and disease spread in the Brazilian Amazon.

The Amazon is Brazil's greatest natural resource and invaluable to the rest of the world as a buffer against climate change. The recent election of Brazil's president brought disputes over development plans for the region back into the spotlight. Historically, the development model for the Amazon has focused on exploitation of natural resources, resulting in environmental degradation, particularly deforestation. Although considerable attention has focused on the long-term global cost of "losing the Amazon," too little attention has focused on the emergence and reemergence of vector-borne diseases that directly impact the local population, with spillover effects to other neighboring areas. We discuss the impact of Amazon development models on human health, with a focus on vector-borne disease risk. We outline policy actions that could mitigate these negative impacts while creating opportunities for environmentally sensitive economic activities.

MGDrivE 2: A simulation framework for gene drive systems incorporating seasonality and epidemiological dynamics.

Interest in gene drive technology has continued to grow as promising new drive systems have been developed in the lab and discussions are moving towards implementing field trials. The prospect of field trials requires models that incorporate a significant degree of ecological detail, including parameters that change over time in response to environmental data such as temperature and rainfall, leading to seasonal patterns in mosquito population density. Epidemiological outcomes are also of growing importance, as: i) the suitability of a gene drive construct for release will depend on its expected impact on disease transmission, and ii) initial field trials are expected to have a measured entomological outcome and a modeled epidemiological outcome. We present MGDrivE 2 (Mosquito Gene Drive Explorer 2): a significant development from the MGDrivE 1 simulation framework that investigates the population dynamics of a variety of gene drive architectures and their spread through spatially-explicit mosquito populations. Key strengths and fundamental improvements of the MGDrivE 2 framework are: i) the ability of parameters to vary with time and induce seasonal population dynamics, ii) an epidemiological module accommodating reciprocal pathogen transmission between humans and mosquitoes, and iii) an implementation framework based on stochastic Petri nets that enables efficient model formulation and flexible implementation. Example MGDrivE 2 simulations are presented to demonstrate the application of the framework to a CRISPR-based split gene drive system intended to drive a disease-refractory gene into a population in a confinable and reversible manner, incorporating time-varying temperature and rainfall data. The simulations also evaluate impact on human disease incidence and prevalence. Further documentation and use examples are provided in vignettes at the project's CRAN repository. MGDrivE 2 is freely available as an open-source R package on CRAN (https://CRAN.R-project.org/package=MGDrivE2). We intend the package to provide a flexible tool capable of modeling gene drive constructs as they move closer to field application and to infer their expected impact on disease transmission.

How public reaction to disease information across scales and the impacts of vector control methods influence disease prevalence and control efficacy.

With the development of social media, the information about vector-borne disease incidence over broad spatial scales can cause demand for local vector control before local risk exists. Anticipatory intervention may still benefit local disease control efforts; however, infection risks are not the only focal concerns governing public demand for vector control. Concern for environmental contamination from pesticides and economic limitations on the frequency and magnitude of control measures also play key roles. Further, public concern may be focused more on ecological factors (i.e., controlling mosquito populations) or on epidemiological factors (i.e., controlling infection-carrying mosquitoes), which may lead to very different control outcomes. Here we introduced a generic Ross-MacDonald model, incorporating these factors under three spatial scales of disease information: local, regional, and global. We tailored and parameterized the model for Zika virus transmitted by Aedes aegypti mosquito. We found that sensitive reactivity caused by larger-scale incidence information could decrease average human infections per patch breeding capacity, however, the associated increase in total control effort plays a larger role, which leads to an overall decrease in control efficacy. The shift of focal concerns from epidemiological to ecological risk could relax the negative effect of the sensitive reactivity on control efficacy when mosquito breeding capacity populations are expected to be large. This work demonstrates that, depending on expected total mosquito breeding capacity population size, and weights of different focal concerns, large-scale disease information can reduce disease infections without lowering control efficacy. Our findings provide guidance for vector-control strategies by considering public reaction through social media.

Aedes-AI: Neural network models of mosquito abundance.

We present artificial neural networks as a feasible replacement for a mechanistic model of mosquito abundance. We develop a feed-forward neural network, a long short-term memory recurrent neural network, and a gated recurrent unit network. We evaluate the networks in their ability to replicate the spatiotemporal features of mosquito populations predicted by the mechanistic model, and discuss how augmenting the training data with time series that emphasize specific dynamical behaviors affects model performance. We conclude with an outlook on how such equation-free models may facilitate vector control or the estimation of disease risk at arbitrary spatial scales.

Time-Scale Analysis and Parameter Fitting for Vector-Borne Diseases with Spatial Dynamics.

Vector-borne diseases are progressively spreading in a growing number of countries, and it has the potential to invade new areas and habitats. From the dynamical perspective, the spatial-temporal interaction of models that try to adjust to such events is rich and challenging. The first challenge is to address the dynamics of vectors (very fast and local) and the dynamics of humans (very heterogeneous and non-local). The objective of this work is to use the well-known Ross-Macdonald models, identifying different time scales, incorporating human spatial movements and estimate in a suitable way the parameters. We will concentrate on a practical example, a simplified space model, and apply it to dengue spread in the state of Rio de Janeiro, Brazil.