Mapping global environmental suitability for Zika virus

Assessing dengue risk globally using non-Markovian models

Dengue is a vector-borne disease transmitted by Aedes mosquitoes. The worldwide spread of these mosquitoes and the increasing disease burden have emphasized the need for a spatio-temporal risk map capable of assessing dengue outbreak conditions and quantifying the outbreak risk. Given that the life cycle of Aedes mosquitoes is strongly influenced by habitat temperature, numerous studies have utilized temperature-dependent development rates of these mosquitoes to construct virus transmission and outbreak risk models. In this study, we contribute to existing research by developing a mechanistic model for the mosquito life cycle that accurately captures its non-Markovian nature. Beginning with integral equations to track the mosquito population across different life cycle stages, we demonstrate how to derive the corresponding differential equations using phase-type distributions. This approach can be further applied to similar non-Markovian processes that are currently described with less accurate Markovian models. By fitting the model to data on human dengue cases, we estimate several model parameters, allowing the development of a global spatiotemporal dengue risk map. This risk model employs temperature and precipitation data to assess the environmental suitability for dengue outbreaks in a given area.

Sex-specific effect of antenatal Zika virus infection on murine fetal growth, placental nutrient transporters, and nutrient sensor signaling pathways

Maternal Zika virus (ZIKV) infection during pregnancy has been associated with severe intrauterine growth restriction (IUGR), placental damage, metabolism disturbances, and newborn neurological abnormalities. Here, we investigated the impact of maternal ZIKV infection on placental nutrient transporters and nutrient-sensitive pathways. Immunocompetent (C57BL/6) mice were injected with Low (103 PFU-ZIKVPE243) or High (5 × 107 PFU-ZIKVPE243) ZIKV titers at gestational day (GD) 12.5, and tissue was collected at GD18.5 (term). Fetal-placental growth was impaired in male fetuses, which exhibited higher placental expression of the ZIKV infective marker, eukaryotic translation initiation factor 2 (eIF2α), but lower levels of phospho-eIF2α. There were no differences in fetal-placental growth in female fetuses, which exhibited no significant alterations in placental ZIKV infective markers. Furthermore, ZIKV promoted increased expression of glucose transporter type 1 (Slc2a1/Glut1) and decreased levels of glucose-6-phosphate in female placentae, with no differences in amino acid transport potential. In contrast, ZIKV did not impact glucose transporters in male placentae but downregulated sodium-coupled neutral amino acid 2 (Snat2) transporter expression. We also observed sex-dependent differences in the hexosamine biosynthesis pathway (HBP) and O-GlcNAcylation in ZIKV-infected pregnancies, showing that ZIKV can disturb placental nutrient sensing. Our findings highlight molecular alterations in the placenta caused by maternal ZIKV infection, shedding light on nutrient transport, sensing, and availability. Our results also suggest that female and male placentae employ distinct coping mechanisms in response to ZIKV-induced metabolic changes, providing insights into therapeutic approaches for congenital Zika syndrome.

Mosquito population dynamics is shaped by the interaction among larval density, season, and land use

Understanding how variation in key abiotic and biotic factors interact at spatial scales relevant for mosquito fitness and population dynamics is crucial for predicting current and future mosquito distributions and abundances, and the transmission potential for human pathogens. However, studies investigating the effects of environmental variation on mosquito traits have investigated environmental factors in isolation or in laboratory experiments that examine constant environmental conditions that often do not occur in the field. To address these limitations, we conducted a semi-field experiment in Athens, Georgia using the invasive Asian tiger mosquito (Aedes albopictus). We selected nine sites that spanned natural variation in impervious surface and vegetation cover to explore effects of the microclimate (temperature and humidity) on mosquitoes. On these sites, we manipulated conspecific larval density at each site. We repeated the experiment in the summer and fall. We then evaluated the effects of land cover, larval density, and time of season, as well as interactive effects, on the mean proportion of females emerging, juvenile development time, size upon emergence, and predicted per capita population growth (i.e., fitness). We found significant effects of larval density, land cover, and season on all response variables. Of most note, we saw strong interactive effects of season and intra-specific density on each response variable, including a non-intuitive decrease in development time with increasing intra-specific competition in the fall. Our study demonstrates that ignoring the interaction between variation in biotic and abiotic variables could reduce the accuracy and precision of models used to predict mosquito population and pathogen transmission dynamics, especially those inferring dynamics at finer-spatial scales across which transmission and control occur.

Anopheles mosquitoes in Morocco: implication for public health and underlined challenges for malaria re-establishment prevention under current and future climate conditions

BACKGROUND

The potential reappearance and/or expansion of vector-borne diseases is one of the terrifying issues awaiting humanity in the context of climate change. The presence of competent Anopheles vectors, as well as suitable environmental circumstances, may result in the re-emergence of autochthonous Malaria, after years of absence. In Morocco, international travel and migration movements from Malaria-endemic areas have recently increased the number of imported cases, raising awareness of Malaria's possible reintroduction. Using machine learning we developed model predictions, under current and future (2050) climate, for the prospective distribution of Anopheles claviger, Anopheles labranchiae, Anopheles multicolor, and Anopheles sergentii implicated or incriminated in Malaria transmission.

RESULTS

All modelled species are expected to find suitable habitats and have the potential to become established in the northern and central parts of the country, under present-day conditions. Distinct changes in the distributions of the four mosquitoes are to be expected under climate change. Even under the most optimistic scenario, all investigated species are likely to acquire new habitats that are now unsuitable, placing further populations in danger. We also observed a northward and altitudinal shift in their distribution towards higher altitudes.

CONCLUSION

Climate change is expected to expand the potential range of malaria vectors in Morocco. Our maps and predictions offer a way to intelligently focus efforts on surveillance and control programmes. To reduce the threat of human infection, it is crucial for public health authorities, entomological surveillance teams, and control initiatives to collaborate and intensify their actions, continuously monitoring areas at risk. © 2023 Society of Chemical Industry.

Effects of climate change and human activities on vector-borne diseases

Vector-borne diseases are transmitted by haematophagous arthropods (for example, mosquitoes, ticks and sandflies) to humans and wild and domestic animals, with the largest burden on global public health disproportionately affecting people in tropical and subtropical areas. Because vectors are ectothermic, climate and weather alterations (for example, temperature, rainfall and humidity) can affect their reproduction, survival, geographic distribution and, consequently, ability to transmit pathogens. However, the effects of climate change on vector-borne diseases can be multifaceted and complex, sometimes with ambiguous consequences. In this Review, we discuss the potential effects of climate change, weather and other anthropogenic factors, including land use, human mobility and behaviour, as possible contributors to the redistribution of vectors and spread of vector-borne diseases worldwide.

Low humidity enhances Zika virus infection and dissemination in Aedes aegypti mosquitoes

As climate change alters Earth's biomes, it is expected the transmission dynamics of mosquito-borne viruses will change. While the effects of temperature changes on mosquito-virus interactions and spread of the pathogens have been elucidated over the last decade, the effects of relative humidity changes are still relatively unknown. To overcome this knowledge gap, we exposed Ae. aegypti females to various low humidity conditions and measured different components of vectorial capacity such as survival, blood-feeding rates, and changes in infection and dissemination of Zika virus. Survival decreased as the humidity level decreased, while infection rates increased as the humidity level decreased. Alternatively, blood feeding rates and dissemination rates peaked at the intermediate humidity level, but returned to the levels of the control at the lowest humidity treatment. These results provide empirical evidence that Ae. aegypti exposure to low humidity can enhance Zika virus infection in the mosquito, which has important implications in predicting how climate change will impact mosquito-borne viruses.

Understanding transmission risk and predicting environmental suitability for Mayaro Virus in Central and South America

Mayaro virus (MAYV) is a mosquito-borne Alphavirus that is widespread in South America. MAYV infection often presents with non-specific febrile symptoms but may progress to debilitating chronic arthritis or arthralgia. Despite the pandemic threat of MAYV, its true distribution remains unknown. The objective of this study was to clarify the geographic distribution of MAYV using an established risk mapping framework. This consisted of generating evidence consensus scores for MAYV presence, modeling the potential distribution of MAYV in select countries across Central and South America, and estimating the population residing in areas suitable for MAYV transmission. We compiled a georeferenced compendium of MAYV occurrence in humans, animals, and arthropods. Based on an established evidence consensus framework, we integrated multiple information sources to assess the total evidence supporting ongoing transmission of MAYV within each country in our study region. We then developed high resolution maps of the disease's estimated distribution using a boosted regression tree approach. Models were developed using nine climatic and environmental covariates that are related to the MAYV transmission cycle. Using the output of our boosted regression tree models, we estimated the total population living in regions suitable for MAYV transmission. The evidence consensus scores revealed high or very high evidence of MAYV transmission in several countries including Brazil (especially the states of Mato Grosso and Goiás), Venezuela, Peru, Trinidad and Tobago, and French Guiana. According to the boosted regression tree models, a substantial region of South America is suitable for MAYV transmission, including north and central Brazil, French Guiana, and Suriname. Some regions (e.g., Guyana) with only moderate evidence of known transmission were identified as highly suitable for MAYV. We estimate that approximately 58.9 million people (95% CI: 21.4-100.4) in Central and South America live in areas that may be suitable for MAYV transmission, including 46.2 million people (95% CI: 17.6-68.9) in Brazil. Our results may assist in prioritizing high-risk areas for vector control, human disease surveillance and ecological studies.

A nonstandard finite difference scheme for a time-fractional model of Zika virus transmission

In this work, we investigate the transmission dynamics of the Zika virus, considering both a compartmental model involving humans and mosquitoes and an extended model that introduces a non-human primate (monkey) as a second reservoir host. The novelty of our approach lies in the later generalization of the model using a fractional time derivative. The significance of this study is underscored by its contribution to understanding the complex dynamics of Zika virus transmission. Unlike previous studies, we incorporate a non-human primate reservoir host into the model, providing a more comprehensive representation of the disease spread. Our results reveal the importance of utilizing a nonstandard finite difference (NSFD) scheme to simulate the disease's dynamics accurately. This NSFD scheme ensures the positivity of the solution and captures the correct asymptotic behavior, addressing a crucial limitation of standard solvers like the Runge-Kutta Fehlberg method (ode45). The numerical simulations vividly demonstrate the advantages of our approach, particularly in terms of positivity preservation, offering a more reliable depiction of Zika virus transmission dynamics. From these findings, we draw the conclusion that considering a non-human primate reservoir host and employing an NSFD scheme significantly enhances the accuracy and reliability of modeling Zika virus transmission. Researchers and policymakers can use these insights to develop more effective strategies for disease control and prevention.

Arbovirus surveillance in pregnant women in north-central Nigeria, 2019-2022

BACKGROUND

The adverse impact of Zika (ZIKV), dengue (DENV), and chikungunya (CHIKV) virus infection in pregnancy has been recognized in Latin America and Asia but is not well studied in Africa. Although originally discovered in sub-Saharan Africa the non-specific clinical presentation of arbovirus infection may have hampered our detection of adverse clinical outcomes and outbreak.

OBJECTIVE

This prospective study of arbovirus infection in pregnant women in north-central Nigeria sought to characterize the prevalence of acute arbovirus infection and determine the impact on pregnancy and infant outcomes.

METHODS

In Nigeria, we screened 1006 pregnant women for ZIKV, DENV and CHIKV IgM/IgG by rapid test (2019-2022). Women with acute infection were recruited for prospective study and infants were examined for any abnormalities from delivery through six months. A subset of rapid test-reactive samples were confirmed using virus-specific ELISAs and neutralization assays.

RESULTS

The prevalence of acute infection (IgM+) was 3.8 %, 9.9 % and 11.8 % for ZIKV, DENV and CHIKV, respectively; co-infections represented 24.5 % of all infections. The prevalence in asymptomatic women was twice the level of symptomatic infection. We found a significant association between acute maternal ZIKV/DENV/CHIKV infection and any gross abnormal birth outcome (p = 0.014).

CONCLUSIONS

Over three rainy seasons, regular acute infection with ZIKV, DENV, and CHIKV was observed with significantly higher rates in pregnant women without symptoms. The potential association arbovirus infection with abnormal birth outcome warrants further prospective study to ascertain the clinical significance of these endemic arboviruses in Africa.

A Large Ensemble Global Dataset for Climate Impact Assessments

We present a self-consistent, large ensemble, high-resolution global dataset of long-term future climate, which accounts for the uncertainty in climate system response to anthropogenic emissions of greenhouse gases and in geographical patterns of climate change. The dataset is developed by applying an integrated spatial disaggregation (SD) - bias-correction (BC) method to climate projections from the MIT Integrated Global System Model (IGSM). Four emission scenarios are considered that represent energy and environmental policies and commitments of potential future pathways, namely, Reference, Paris Forever, Paris 2 °C and Paris 1.5 °C. The dataset contains nine key meteorological variables on a monthly scale from 2021 to 2100 at a spatial resolution of 0.5°x 0.5°, including precipitation, air temperature (mean, minimum and maximum), near-surface wind speed, shortwave and longwave radiation, specific humidity, and relative humidity. We demonstrate the dataset's ability to represent climate-change responses across various regions of the globe. This dataset can be used to support regional-scale climate-related impact assessments of risk across different applications that include hydropower, water resources, ecosystem, agriculture, and sustainable development.

A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk

BACKGROUND

Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used.

METHODS

We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.).

RESULTS

We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures.

CONCLUSIONS

Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping.

Dehydration induced AePer50 regulates midgut infection in Ae. aegypti

In the face of climate change, mosquitoes will experience evolving climates including longer periods of drought. An important physiological response to dry environments is the protection against water loss or dehydration, here defined as desiccation tolerance. Various environmental factors including temperature are known to alter interactions between the mosquito, Aedes aegypti , and the arboviruses it transmits, but little is known about how low humidity impacts arboviral infection. Here, we report that a gene upregulated in response to desiccation is important for controlling midgut infection. We have identified two genetically diverse lines of Ae. aegypti with marked differences in desiccation tolerance. To understand if the genetic basis underlying desiccation tolerance is the same between the contrasting lines, we compared gene expression profiles between desiccant treated and non-desiccant treated individuals in both the desiccation tolerant and susceptible lines by RNAseq. Gene expression analysis demonstrates that different genes are differentially expressed in response to desiccation stress between desiccation tolerant and susceptible lines. The most highly expressed transcript under desiccation stress in the desiccation susceptible line encodes a peritrophin protein, Ae Per50. Peritrophins play a crucial role in peritrophic matrix formation after a bloodmeal. Gene silencing of Ae Per50 by RNAi demonstrates that expression of Ae Per50 is required for survival of the desiccation susceptible line under desiccation stress, but not for the desiccation tolerant line. Moreover, the knockdown of Ae Per50 results in higher infection rates and viral replication rates of ZIKV and higher infection rates of CHIKV. Finally, following a bloodmeal, the desiccation susceptible line develops a thicker peritrophic matrix than the desiccation tolerant line. Together these results provide a functional link between the protection against desiccation and midgut infection which has important implications in predicting how climate change will impact mosquito-borne viruses.

Arbovirus surveillance in pregnant women in north-central Nigeria, 2019-2022

The adverse impact of Zika (ZIKV), dengue (DENV), and chikungunya (CHIKV) virus infection in pregnancy has been recognized in Latin America and Asia but is not well studied in Africa. In Nigeria, we screened 1006 pregnant women for ZIKV, DENV and CHIKV IgM/IgG by rapid test (2019-2022). Women with acute infection were recruited for prospective study and infants were examined for any abnormalities from delivery through six months. A subset of rapid test-reactive samples were confirmed using virus-specific ELISAs and neutralization assays. Prevalence of acute infection (IgM+) was 3.8%, 9.9% and 11.8% for ZIKV, DENV and CHIKV, respectively; co-infections represented 24.5% of all infections. Prevalence in asymptomatic women was twice the level of symptomatic infection. We found a significant association between acute maternal ZIKV/DENV/CHIKV infection and any gross abnormal birth outcome (p=0.014). Further prospective studies will contribute to our understanding of the clinical significance of these endemic arboviruses in Africa.

Concomitant knockdown resistance allele, L982W + F1534C, in Aedes aegypti has the potential to impose fitness costs without selection pressure

The Aedes aegypti mosquito, is an arbovirus vector that can spread dengue, chikungunya, Zika, and yellow fever. Pyrethroids are widely used to control mosquitoes. The voltage-gated sodium channel (Vgsc) is the target of pyrethroids, and amino acid substitutions in this channel attenuate the effects of pyrethroids. This is known as knockdown resistance (kdr). Recently, we found that Ae. aegypti with concomitant Vgsc mutations L982W + F1534C exhibit extremely high levels of pyrethroid resistance. L982 is located in a highly conserved region of Vgsc in vertebrates and invertebrates. This study aimed to evaluate the viability of Ae. aegypti, with concomitant L982W + F1534C mutations in Vgsc. We crossed a resistant strain (FTWC) with a susceptible strain (SMK) and reared it up to 15 generations. We developed a rapid and convenient genotyping method using a fluorescent probe (Eprobe) to easily and accurately distinguish between three genotypes: wild-type and mutant homozygotes, and heterozygotes. As generations progressed, the proportion of wild-type homozygotes increased, and only 2.9% of mutant homozygotes were present at the 15th generation; the allele frequencies of L982W + F1534C showed a decreasing trend over generations. These observations show that these concomitant mutations have some fitness costs, suggesting that mosquitoes can potentially recover pyrethroid susceptibility over time without pyrethroid selection pressure in the field.

Global transmission suitability maps for dengue virus transmitted by Aedes aegypti from 1981 to 2019

Mosquito-borne viruses increasingly threaten human populations due to accelerating changes in climate, human and mosquito migration, and land use practices. Over the last three decades, the global distribution of dengue has rapidly expanded, causing detrimental health and economic problems in many areas of the world. To develop effective disease control measures and plan for future epidemics, there is an urgent need to map the current and future transmission potential of dengue across both endemic and emerging areas. Expanding and applying Index P, a previously developed mosquito-borne viral suitability measure, we map the global climate-driven transmission potential of dengue virus transmitted by Aedes aegypti mosquitoes from 1981 to 2019. This database of dengue transmission suitability maps and an R package for Index P estimations are offered to the public health community as resources towards the identification of past, current and future transmission hotspots. These resources and the studies they facilitate can contribute to the planning of disease control and prevention strategies, especially in areas where surveillance is unreliable or non-existent.

Machine Learning Modeling of Aedes albopictus Habitat Suitability in the 21st Century

The Asian tiger mosquito, Aedes albopictus, is an important vector of arboviruses that cause diseases such as dengue, chikungunya, and zika. The vector is highly invasive and adapted to survive in temperate northern territories outside its native tropical and sub-tropical range. Climate and socio-economic change are expected to facilitate its range expansion and exacerbate the global vector-borne disease burden. To project shifts in the global habitat suitability of the vector, we developed an ensemble machine learning model, incorporating a combination of a Random Forest and XGBoost binary classifiers, trained with a global collection of vector surveillance data and an extensive set of climate and environmental constraints. We demonstrate the reliable performance and wide applicability of the ensemble model in comparison to the known global presence of the vector, and project that suitable habitats will expand globally, most significantly in the northern hemisphere, putting at least an additional billion people at risk of vector-borne diseases by the middle of the 21st century. We project several highly populated areas of the world will be suitable for Ae. albopictus populations, such as the northern parts of the USA, Europe, and India by the end of the century, which highlights the need for coordinated preventive surveillance efforts of potential entry points by local authorities and stakeholders.

Long-term projections of the impacts of warming temperatures on Zika and dengue risk in four Brazilian cities using a temperature-dependent basic reproduction number

For vector-borne diseases the basic reproduction number [Formula: see text], a measure of a disease's epidemic potential, is highly temperature-dependent. Recent work characterizing these temperature dependencies has highlighted how climate change may impact geographic disease spread. We extend this prior work by examining how newly emerging diseases, like Zika, will be impacted by specific future climate change scenarios in four diverse regions of Brazil, a country that has been profoundly impacted by Zika. We estimated a [Formula: see text], derived from a compartmental transmission model, characterizing Zika (and, for comparison, dengue) transmission potential as a function of temperature-dependent biological parameters specific to Aedes aegypti. We obtained historical temperature data for the five-year period 2015-2019 and projections for 2045-2049 by fitting cubic spline interpolations to data from simulated atmospheric data provided by the CMIP-6 project (specifically, generated by the GFDL-ESM4 model), which provides projections under four Shared Socioeconomic Pathways (SSP). These four SSP scenarios correspond to varying levels of climate change severity. We applied this approach to four Brazilian cities (Manaus, Recife, Rio de Janeiro, and São Paulo) that represent diverse climatic regions. Our model predicts that the [Formula: see text] for Zika peaks at 2.7 around 30°C, while for dengue it peaks at 6.8 around 31°C. We find that the epidemic potential of Zika will increase beyond current levels in Brazil in all of the climate scenarios. For Manaus, we predict that the annual [Formula: see text] range will increase from 2.1-2.5, to 2.3-2.7, for Recife we project an increase from 0.4-1.9 to 0.6-2.3, for Rio de Janeiro from 0-1.9 to 0-2.3, and for São Paulo from 0-0.3 to 0-0.7. As Zika immunity wanes and temperatures increase, there will be increasing epidemic potential and longer transmission seasons, especially in regions where transmission is currently marginal. Surveillance systems should be implemented and sustained for early detection.

Recent Advancements in Mosquito-Borne Flavivirus Vaccine Development

Lately, the global incidence of flavivirus infection has been increasing dramatically and presents formidable challenges for public health systems around the world. Most clinically significant flaviviruses are mosquito-borne, such as the four serotypes of dengue virus, Zika virus, West Nile virus, Japanese encephalitis virus and yellow fever virus. Until now, no effective antiflaviviral drugs are available to fight flaviviral infection; thus, a highly immunogenic vaccine would be the most effective weapon to control the diseases. In recent years, flavivirus vaccine research has made major breakthroughs with several vaccine candidates showing encouraging results in preclinical and clinical trials. This review summarizes the current advancement, safety, efficacy, advantages and disadvantages of vaccines against mosquito-borne flaviviruses posing significant threats to human health.

Aedes aegypti in Southern Brazil: Spatiotemporal Distribution Dynamics and Association with Climate and Environmental Factors

In Brazil, the mosquito Aedes (Stegomyia) aegypti is considered the main vector of the dengue, chikungunya, and Zika arbovirus transmission. Recent epidemiological studies in southern Brazil have shown an increase in the incidence of dengue, raising concerns over epidemiological control, monitoring, and surveys. Therefore, this study aimed at performing a historical spatiotemporal analysis of the Ae. aegypti house indices (HI) in southern Brazil over the last 19 years. As vector infestation was associated with climatic and environmental variables, HI data from the Brazilian Ministry of Health, climate data from the Giovanni web-based application, and environmental data from the Mapbiomas project were used in this study. Our results showed an expressive increase in the number of HI surveys in the municipalities confirming the vector presence, as compared to those in 2017. Environmental variables, such as urban infrastructure, precipitation, temperature, and humidity, were positively correlated with the Ae. aegypti HI. This was the first study to analyze Ae. aegypti HI surveys in municipalities of southern Brazil, and our findings could help in developing and planning disease control strategies to improve public health.

Antibody seropositivity and endemicity of chikungunya and Zika viruses in Nigeria

Mosquito-borne infections are of global health concern because of their rapid spread and upsurge, which creates a risk for coinfections. chikungunya virus (CHIKV), an arbovirus disease transmitted by Aedes aegypti or A. albopictus, and malaria, a parasitic disease transmitted by Anopheles gambiae, are prevalent in Nigeria and neighbouring countries, but their burden and possible coinfections are poorly understood. In this study, we investigated the antibody seropositivity and endemicity of chikungunya and Zika viruses (ZIKV) in three regions of Nigeria. A cross-sectional sero-survey was conducted on 871 participants. Samples were collected from outpatients by simple random sampling. Analyses of the samples were performed using recomLine Tropical Fever for the presence of antibody serological marker IgG immunoblot with CHIKV VLP (virus like particle), ZIKV NS1 and ZIKV Equad according to manufacturers’ instructions and malaria RDT for malaria parasite. There was a significantly higher antibody seropositivity against CHIKV in the central region than in the northern and southern regions (69.5%, 291/419), while ZIKV-seropositivity (22.4%, 34/152) and CHIKV-ZIKV co-circulating antibody seropositivity (17.8%, 27/152) were notably higher in the southern region than in the central and northern regions. This investigation revealed an unexpectedly high antibody seropositivity and concealed endemicity of CHIKV and ZIKV in three Nigerian regions. The seropositivity of detectable antibodies differed among the three geographical locations.

Current Advances in Zika Vaccine Development

Zika virus (ZIKV), an emerging arthropod-borne flavivirus, was first isolated in Uganda in 1947 from monkeys and first detected in humans in Nigeria in 1952; it has been associated with a dramatic burden worldwide. Since then, interventions to reduce the burden of ZIKV infection have been mainly restricted to mosquito control, which in the end proved to be insufficient by itself. Hence, the situation prompted scientists to increase research on antivirals and vaccines against the virus. These efforts are still ongoing as the pathogenesis and immune evasion mechanisms of ZIKV have not yet been fully elucidated. Understanding the viral disease mechanism will provide a better landscape to develop prophylactic and therapeutic strategies against ZIKV. Currently, no specific vaccines or drugs have been approved for ZIKV. However, some are undergoing clinical trials. Notably, different platforms have been evaluated for the design of vaccines, including DNA, mRNA, viral vectors, virus-like particles (VLPs), inactivated virus, live attenuated virus, peptide and protein-based vaccines, passive immunizations by using monoclonal antibodies (MAbs), and vaccines that target vector-derived antigens. These vaccines have been shown to induce specific humoral and cellular immune responses and reduce viremia and viral RNA titers, both in vitro and in vivo. This review provides a comprehensive summary of current advancements in the development of vaccines against Zika virus.

Climate-driven mosquito-borne viral suitability index: measuring risk transmission of dengue, chikungunya and Zika in Mexico

Background

Climate variability influences the population dynamics of the Aedes aegypti mosquito that transmits the viruses that cause dengue, chikungunya and Zika. In recent years these diseases have grown considerably. Dengue is now the fastest-growing mosquito-transmitted disease worldwide, putting 40 per cent of the global population at risk. With no effective antiviral treatments or vaccines widely available, controlling mosquito population remains one of the most effective ways to prevent epidemics. This paper analyses the temporal and spatial dynamics of dengue in Mexico during 2000–2020 and that of chikungunya and Zika since they first appeared in the country in 2014 and 2015, respectively. This study aims to evaluate how seasonal climatological variability affects the potential risk of transmission of these mosquito-borne diseases. Mexico is among the world’s most endemic countries in terms of dengue. Given its high incidence of other mosquito-borne diseases and its size and wide range of climates, it is a good case study.

Methods

We estimate the recently proposed mosquito-borne viral suitability index P, which measures the transmission potential of mosquito-borne pathogens. This index mathematically models how humidity, temperature and precipitation affect the number of new infections generated by a single infected adult female mosquito in a host population. We estimate this suitability index across all Mexico, at small-area level, on a daily basis during 2000–2020.

Results

We find that the index P predicted risk transmission is strongly correlated with the areas and seasons with a high incidence of dengue within the country. This correlation is also high enough for chikungunya and Zika in Mexico. We also show the index P is sensitive to seasonal climatological variability, including extreme weather shocks.

Conclusions

The paper shows the dynamics of dengue, chikungunya and Zika in Mexico are strongly associated with seasonal climatological variability and the index P. This potential risk of transmission index, therefore, is a valuable tool for surveillance for mosquito-borne diseases, particularly in settings with varied climates and limited entomological capacity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12942-022-00317-0.

Prospective surveillance of Zika virus at the end of the Americas’ outbreak: An unexpected outcome

Objective

The French Polynesia Zika virus (ZIKV) outbreak (2013–2014) broadened the known manifestations of ZIKV disease (ZVD) to include neural and congenital syndromes. A subsequent epidemic in the Americas (2015–2016) caused >685,000 suspected/confirmed cases before seemingly disappearing as fast as it expanded. A study was implemented (2017–2018) to detect ZVD cases in the region (Mexico, Honduras, Colombia and Puerto Rico), with the aim of validating surveillance methodology so as to increase sensitivity in case detection, which would have potential application for future vaccine development endeavors.

Study design and setting

To identify potential cases, we focused on signs/symptoms that were frequently associated with ZVD for confirmation by PCR. Serostatus and seroconversion were evaluated by ZIKV non-structural protein 1 blockade-of-binding enzyme-linked immunosorbent assay (BOB ELISA) and microneutralization assay.

Results

Overall, 2,400 participants aged 15–40 years were enrolled; 959 (40.0%) had signs/symptoms that could be associated with ZVD: axillary temperature ≥37°C (64.3%), myalgia (60.8%) and arthralgia (58.6%). Three suspected cases were virologically confirmed. Zika seroprevalence was high at study initiation (52.6% [BOB ELISA] and 56.0% [microneutralization assay]). In participants who were Zika seronegative, low seroconversion rates were observed after one year follow-up (3.6% [BOB ELISA] and 3.1% [microneutralization assay]).

Conclusion

The ZIKV continued to circulate in the Americas at very low levels following the 2015–2016 outbreak. The epidemiological factors driving Zika’s rapid rise and decline remain poorly understood.

Clinical trial registration

Clinicaltrials.gov: NCT03158233 BARDA (Contract # HHSO100201600039C) WHO Universal Trial Number: U1111-1183-5687.

The global trends and regional differences in incidence of Zika virus infection and implications for Zika virus infection prevention

Background

Zika virus (ZIKV) infection has potential result in severe birth effects. An improved understanding of global trend and regional differences is needed.

Methods

Annual ZIKV infection episodes and incidence rates were collected from Global Burden of Disease Study 2019. Episodes changes and estimated annual percentage changes (EAPCs) of age-standardized incidence rate (ASR) were calculated. Top passenger airport-pairs were obtained from the International Air Transport Association to understand places susceptible to imported ZIKV cases.

Results

Globally, the ASR increased by an average of 72.85% (95%CI: 16.47% to 156.53%) per year from 2011 to 2015 and subsequently decreased from 20.25 per 100,000 in 2015 to 3.44 per 100,000 in 2019. Most of ZIKV infections clustered in Latin America. The proportion of episodes in Central and Tropical Latin America decreased in 2019 with sporadic episodes elsewhere. High Socio-Demographic Index (SDI) regions had more episodes in 2019 than in 2015. Additionally, 15–49 years group had the largest proportion of episodes, females had a higher number of episodes, and a higher incidence rate of 70 plus group was observed in males than females. Certain cities in Europe, North America and Latin America/Caribbean had a high population mobility in ZIKV outbreak areas considered a high risk of imported cases.

Conclusions

ZIKV infection is still a public health threat in Latin America and Caribbean and high SDI regions suffered an increasing trend of ZIKV infection. Interventions such as development of surveillance networks and vector-control should be attached to ZIKV control in these key regions. Reproductive suggestions should be taken to reduce ZIKV-related birth defects for the people of reproductive age who are facing a higher threat of ZIKV infection, especially females. Moreover, surveillance of travellers is needed to reverse the uptrends of travel-related imported ZIKV infection. More studies focusing on ZIKV should be performed to make targeted and effective prevention strategies in the future.

Zika virus (ZIKV) infection is a mosquito-borne illness and has potential result in severe birth effects. Currently, ZIKV is still causing an unprecedented ongoing epidemic in Latin America and threatening North America and potentially the rest of the world. This is the first study to assess the global landscape, long-term trends and regional differences in the incidence of ZIKV infection using the data from Global Burden of Disease (GBD) Study 2019, including the description of ZIKV infection episodes by different sex and by different year group, as well as the relationship between international travellers and imported ZIKV cases. Our study can not only serve as complement to previous studies, but also provide a more comprehensive perspective of global ZIKV infection prevention strategies.

Interactions between seasonal temperature variation and temporal synchrony drive increased arbovirus co-infection incidence

Though instances of arthropod-borne (arbo)virus co-infection have been documented clinically, the overall incidence of arbovirus co-infection and its drivers are not well understood. Now that dengue, Zika and chikungunya viruses are all in circulation across tropical and subtropical regions of the Americas, it is important to understand the environmental and biological conditions that make co-infections more likely to occur. To understand this, we developed a mathematical model of co-circulation of two arboviruses, with transmission parameters approximating dengue, Zika and/or chikungunya viruses, and co-infection possible in both humans and mosquitoes. We examined the influence of seasonal timing of arbovirus co-circulation on the extent of co-infection. By undertaking a sensitivity analysis of this model, we examined how biological factors interact with seasonality to determine arbovirus co-infection transmission and prevalence. We found that temporal synchrony of the co-infecting viruses and average temperature were the most influential drivers of co-infection incidence. Our model highlights the synergistic effect of co-transmission from mosquitoes, which leads to more than double the number of co-infections than would be expected in a scenario without co-transmission. Our results suggest that appreciable numbers of co-infections are unlikely to occur except in tropical climates when the viruses co-occur in time and space.

Recent Progress in the Discovery and Development of Monoclonal Antibodies against Viral Infections

Monoclonal antibodies (mAbs), the new revolutionary class of medications, are fast becoming tools against various diseases thanks to a unique structure and function that allow them to bind highly specific targets or receptors. These specialized proteins can be produced in large quantities via the hybridoma technique introduced in 1975 or by means of modern technologies. Additional methods have been developed to generate mAbs with new biological properties such as humanized, chimeric, or murine. The inclusion of mAbs in therapeutic regimens is a major medical advance and will hopefully lead to significant improvements in infectious disease management. Since the first therapeutic mAb, muromonab-CD3, was approved by the U.S. Food and Drug Administration (FDA) in 1986, the list of approved mAbs and their clinical indications and applications have been proliferating. New technologies have been developed to modify the structure of mAbs, thereby increasing efficacy and improving delivery routes. Gene delivery technologies, such as non-viral synthetic plasmid DNA and messenger RNA vectors (DMabs or mRNA-encoded mAbs), built to express tailored mAb genes, might help overcome some of the challenges of mAb therapy, including production restrictions, cold-chain storage, transportation requirements, and expensive manufacturing and distribution processes. This paper reviews some of the recent developments in mAb discovery against viral infections and illustrates how mAbs can help to combat viral diseases and outbreaks.

An Evaluation of the OpenWeatherMap API versus INMET Using Weather Data from Two Brazilian Cities: Recife and Campina Grande

Certain weather conditions are inadvertently related to increased population of various mosquitoes. In order to predict the burden of mosquito populations in the Global South, it is imperative to integrate weather-related risk factors into such predictive models. There are a lot of online open-source weather platforms that provide historical, current and future weather forecasts which can be utilised for general predictions, and these electronic sources serve as an alternate option for weather data when physical weather stations are inaccessible (or inactive). Before using data from such online source, it is important to assess the accuracy against some baseline measure. In this paper, we therefore evaluated the accuracy and suitability of weather forecasts of two parameters namely temperature and humidity from the OpenWeatherMap API (an online weather platform) and compared them with actual measurements collected from the Brazilian weather stations (INMET). The evaluation was focused on two Brazilian cites, namely, Recife and Campina Grande. The intention is to prepare an early warning model which will harness data from OpenWeatherMap API for mosquito prediction.

Epidemic characteristics and transmission risk prediction of brucellosis in Xi'an city, Northwest China

Human brucellosis (HB) has re-emerged in China since the mid-1990s, and exhibited an apparent geographic expansion shifted from the traditional livestock regions to the inland areas of China. It is often neglected in non-traditional epidemic areas, posing a serious threat to public health in big cities. We carried out a retrospective epidemiological study in Xi'an, the largest city in northwestern China. It utilizes long-term surveillance data on HB during 2008–2021 and investigation data during 2014–2021. A total of 1989 HB cases were reported in Xi'an, consisting of 505 local cases, i.e., those located in Xi'an and 1,484 non-local cases, i.e., those located in other cities. Significantly epidemiological heterogeneity was observed between them, mainly owing to differences in the gender, occupation, diagnostic delays, and reporting institutions. Serological investigations suggested that 59 people and 1,822 animals (sheep, cattle, and cows) tested positive for brucellosis from 2014 to 2021, with the annual average seroprevalence rates were 1.38 and 1.54%, respectively. The annual animal seroprevalence rate was positively correlated with the annual incidence of non-local HB cases. Multivariate boosted regression tree models revealed that gross domestic product, population density, length of township roads, number of farms, and nighttime lights substantially contributed to the spatial distribution of local HB. Approximately 7.84 million people inhabited the potential infection risk zones in Xi'an. Our study highlights the reemergence of HB in non-epidemic areas and provides a baseline for large and medium-sized cities to identify regions, where prevention and control efforts should be prioritized in the future.

Spatial Analysis of Mosquito-Borne Diseases in Europe: A Scoping Review

Mosquito-borne infections are increasing in endemic areas and previously unaffected regions. In 2020, the notification rate for Dengue was 0.5 cases per 100,000 population, and for Chikungunya <0.1/100,000. In 2019, the rate for Malaria was 1.3/100,000, and for West Nile Virus, 0.1/100,000. Spatial analysis is increasingly used in surveillance and epidemiological investigation, but reviews about their use in this research topic are scarce. We identify and describe the methodological approaches used to investigate the distribution and ecological determinants of mosquito-borne infections in Europe. Relevant literature was extracted from PubMed, Scopus, and Web of Science from inception until October 2021 and analysed according to PRISMA-ScR protocol. We identified 110 studies. Most used geographical correlation analysis (n = 50), mainly applying generalised linear models, and the remaining used spatial cluster detection (n = 30) and disease mapping (n = 30), mainly conducted using frequentist approaches. The most studied infections were Dengue (n = 32), Malaria (n = 26), Chikungunya (n = 26), and West Nile Virus (n = 24), and the most studied ecological determinants were temperature (n = 39), precipitation (n = 24), water bodies (n = 14), and vegetation (n = 11). Results from this review may support public health programs for mosquito-borne disease prevention and may help guide future research, as we recommended various good practices for spatial epidemiological studies.

Comparing sources of mobility for modelling the epidemic spread of Zika virus in Colombia

Timely, accurate, and comparative data on human mobility is of paramount importance for epidemic preparedness and response, but generally not available or easily accessible. Mobile phone metadata, typically in the form of Call Detail Records (CDRs), represents a powerful source of information on human movements at an unprecedented scale. In this work, we investigate the potential benefits of harnessing aggregated CDR-derived mobility to predict the 2015-2016 Zika virus (ZIKV) outbreak in Colombia, when compared to other traditional data sources. To simulate the spread of ZIKV at sub-national level in Colombia, we employ a stochastic metapopulation epidemic model for vector-borne diseases. Our model integrates detailed data on the key drivers of ZIKV spread, including the spatial heterogeneity of the mosquito abundance, and the exposure of the population to the virus due to environmental and socio-economic factors. Given the same modelling settings (i.e. initial conditions and epidemiological parameters), we perform in-silico simulations for each mobility network and assess their ability in reproducing the local outbreak as reported by the official surveillance data. We assess the performance of our epidemic modelling approach in capturing the ZIKV outbreak both nationally and sub-nationally. Our model estimates are strongly correlated with the surveillance data at the country level (Pearson’s r = 0.92 for the CDR-informed network). Moreover, we found strong performance of the model estimates generated by the CDR-informed mobility networks in reproducing the local outbreak observed at the sub-national level. Compared to the CDR-informed networks, the performance of the other mobility networks is either comparatively similar or substantially lower, with no added value in predicting the local epidemic. This suggests that mobile phone data captures a better picture of human mobility patterns. This work contributes to the ongoing discussion on the value of aggregated mobility estimates from CDRs data that, with appropriate data protection and privacy safeguards, can be used for social impact applications and humanitarian action.

Utilizing citizen science to model the distribution of Aedes aegypti in West Africa

In the rapidly urbanizing region of West Africa, Aedes mosquitoes pose an emerging threat of infectious disease that is compounded by limited vector surveillance. Citizen science has been proposed as a way to fill surveillance gaps by training local residents to collect and share information on disease vectors. Understanding the distribution of arbovirus vectors in West Africa can inform researchers and public health officials on where to conduct disease surveillance and focus public health interventions. We utilized citizen science data collected through NASA's GLOBE Observer mobile phone application and data from a previously published literature review on Aedes mosquito distribution to examine the contribution of citizen science to understanding the distribution of Ae. aegypti in West Africa using Maximum Entropy modeling. Combining citizen science and literature-derived observations improved the fit of the model compared to models created by each data source alone but did not alleviate location bias within the models, likely due to lack of widespread observations. Understanding Ae. aegypti distribution will require greater investment in Aedes mosquito surveillance in the region, and citizen science should be utilized as a tool in this mission to increase the reach of surveillance.

Mapping the Distributions of Mosquitoes and Mosquito-Borne Arboviruses in China

The geographic expansion of mosquitos is associated with a rising frequency of outbreaks of mosquito-borne diseases (MBD) worldwide. We collected occurrence locations and times of mosquito species, mosquito-borne arboviruses, and MBDs in the mainland of China in 1954−2020. We mapped the spatial distributions of mosquitoes and arboviruses at the county level, and we used machine learning algorithms to assess contributions of ecoclimatic, socioenvironmental, and biological factors to the spatial distributions of 26 predominant mosquito species and two MBDs associated with high disease burden. Altogether, 339 mosquito species and 35 arboviruses were mapped at the county level. Culex tritaeniorhynchus is found to harbor the highest variety of arboviruses (19 species), followed by Anopheles sinensis (11) and Culex pipiens quinquefasciatus (9). Temperature seasonality, annual precipitation, and mammalian richness were the three most important contributors to the spatial distributions of most of the 26 predominant mosquito species. The model-predicted suitable habitats are 60–664% larger in size than what have been observed, indicating the possibility of severe under-detection. The spatial distribution of major mosquito species in China is likely to be under-estimated by current field observations. More active surveillance is needed to investigate the mosquito species in specific areas where investigation is missing but model-predicted probability is high.

Spatiotemporal distribution and insecticide resistance status of Aedes aegypti in Ghana

Background

Vector control is the main intervention used to control arboviral diseases transmitted by Aedes mosquitoes because there are no effective vaccines or treatments for most of them. Control of Aedes mosquitoes relies heavily on the use of insecticides, the effectiveness of which may be impacted by resistance. In addition, rational insecticide application requires detailed knowledge of vector distribution, dynamics, resting, and feeding behaviours, which are poorly understood for Aedes mosquitoes in Africa. This study investigated the spatiotemporal distribution and insecticide resistance status of Aedes aegypti across ecological extremes of Ghana.

Methods

Immature mosquitoes were sampled from containers in and around human dwellings at seven study sites in urban, suburban, and rural areas of Ghana. Adult Aedes mosquitoes were sampled indoors and outdoors using Biogents BG-Sentinel 2 mosquito traps, human landing catches, and Prokopack aspiration. Distributions of immature and adult Aedes mosquitoes were determined indoors and outdoors during dry and rainy seasons at all sites. The phenotypic resistance status of Aedes mosquitoes to insecticides was determined using World Health Organization susceptibility bioassays. The host blood meal source was determined by polymerase chain reaction.

Results

A total of 16,711 immature Aedes were sampled, with over 70% found in car tyres. Significantly more breeding containers had Aedes immatures during the rainy season (11,856; 70.95%) compared to the dry season (4855; 29.05%). A total of 1895 adult Aedes mosquitos were collected, including Aedes aegypti (97.8%), Aedes africanus (2.1%) and Aedesluteocephalus (0.1%). Indoor sampling of adult Aedes yielded a total of 381 (20.1%) and outdoor sampling a total of 1514 (79.9%) mosquitoes (z = − 5.427, P = 0.0000) over the entire sampling period. Aedes aegypti populations were resistant to dichlorodiphenyltrichloroethane at all study sites. Vectors showed suspected resistance to bendiocarb (96–97%), permethrin (90–96%) and deltamethrin (91–96%), and were susceptible to the organophosphate for all study sites. Blood meal analysis showed that the Aedes mosquitoes were mostly anthropophilic, with a human blood index of 0.9 (i.e. humans, 90%; human and dog, 5%; dog and cow, 5%).

Conclusions

Aedes mosquitoes were found at high densities in all ecological zones of Ghana. Resistance of Aedes spp. to pyrethroids and carbamates may limit the efficacy of vector control programmes and thus requires careful monitoring.

Graphical abstract

On the use of inhibitors of 4-hydroxyphenylpyruvate dioxygenase as a vector-selective insecticide in the control of mosquitoes

BACKGROUND

Blood-sucking insects incorporate many times their body weight of blood in a single meal. Because proteins are the major component of vertebrate blood, its digestion in the gut generates extremely high concentrations of free amino acids. Previous reports showed that the tyrosine degradation pathway plays an essential role in adapting these animals to blood feeding. Inhibition of 4-hydroxyphenylpyruvate dioxygenase (HPPD), the rate-limiting step of tyrosine degradation, results in the death of insects after a blood meal. Therefore, it has been suggested that compounds that block the catabolism of tyrosine could act selectively on blood-feeding insects. Here, we evaluated the toxicity against mosquitoes of three HPPD inhibitors currently used as herbicides and in human health.

RESULTS

Of the compounds tested, nitisinone (NTBC) proved to be more potent than mesotrione (MES) and isoxaflutole (IFT) in Aedes aegypti. NTBC was lethal to Ae. aegypti in artificial feeding assays [median lethal dose (LD₅₀ ): 4.53 μm] and in topical application (LD₅₀ : 0.012 nmol/mosquito). NTBC was also lethal to Ae. aegypti populations that were resistant to neurotoxic insecticides, and to other mosquito species (Anopheles and Culex).

CONCLUSION

HPPD inhibitors, particularly NTBC, represent promising new drugs for mosquito control. Because they affect only blood-feeding organisms, they represent a safer and more environmentally friendly alternative to conventional neurotoxic insecticides. © 2021 Society of Chemical Industry.

Mapping environmental suitability of Haemagogus and Sabethes spp. mosquitoes to understand sylvatic transmission risk of yellow fever virus in Brazil

BACKGROUND

Yellow fever (YF) is an arboviral disease which is endemic to Brazil due to a sylvatic transmission cycle maintained by infected mosquito vectors, non-human primate (NHP) hosts, and humans. Despite the existence of an effective vaccine, recent sporadic YF epidemics have underscored concerns about sylvatic vector surveillance, as very little is known about their spatial distribution. Here, we model and map the environmental suitability of YF's main vectors in Brazil, Haemagogus spp. and Sabethes spp., and use human population and NHP data to identify locations prone to transmission and spillover risk.

METHODOLOGY/PRINCIPAL FINDINGS

We compiled a comprehensive set of occurrence records on Hg. janthinomys, Hg. leucocelaenus, and Sabethes spp. from 1991-2019 using primary and secondary data sources. Linking these data with selected environmental and land-cover variables, we adopted a stacked regression ensemble modelling approach (elastic-net regularized GLM, extreme gradient boosted regression trees, and random forest) to predict the environmental suitability of these species across Brazil at a 1 km x 1 km resolution. We show that while suitability for each species varies spatially, high suitability for all species was predicted in the Southeastern region where recent outbreaks have occurred. By integrating data on NHP host reservoirs and human populations, our risk maps further highlight municipalities within the region that are prone to transmission and spillover.

CONCLUSIONS/SIGNIFICANCE

Our maps of sylvatic vector suitability can help elucidate potential locations of sylvatic reservoirs and be used as a tool to help mitigate risk of future YF outbreaks and assist in vector surveillance. Furthermore, at-risk regions identified from our work could help disease control and elucidate gaps in vaccination coverage and NHP host surveillance.

Dysregulated metabolism underpins Zika-virus-infection-associated impairment in fetal development

Zika virus (ZIKV) is an Aedes-mosquito-borne flavivirus that causes debilitating congenital and developmental disorders. Improved understanding of ZIKV pathogenesis could assist efforts to fill the therapeutic and vaccine gap. We use several ZIKV strains, including a pair differing by a single phenylalanine-to-leucine substitution (M-F37L) in the membrane (M) protein, coupled with unbiased genomics to demarcate the border between attenuated and pathogenic infection. We identify infection-induced metabolic dysregulation as a minimal set of host alterations that differentiates attenuated from pathogenic ZIKV strains. Glycolytic rewiring results in impaired oxidative phosphorylation and mitochondrial dysfunction that trigger inflammation and apoptosis in pathogenic but not attenuated ZIKV strains. Critically, pyruvate supplementation prevents cell death, in vitro, and rescues fetal development in ZIKV-infected dams. Our findings thus demonstrate dysregulated metabolism as an underpinning of ZIKV pathogenicity and raise the potential of pyruvate supplementation in expectant women as a prophylaxis against congenital Zika syndrome.

Challenges in evaluating risks and policy options around endemic establishment or elimination of novel pathogens

When a novel pathogen emerges there may be opportunities to eliminate transmission - locally or globally - whilst case numbers are low. However, the effort required to push a disease to elimination may come at a vast cost at a time when uncertainty is high. Models currently inform policy discussions on this question, but there are a number of open challenges, particularly given unknown aspects of the pathogen biology, the effectiveness and feasibility of interventions, and the intersecting political, economic, sociological and behavioural complexities for a novel pathogen. In this overview, we detail how models might identify directions for better leveraging or expanding the scope of data available on the pathogen trajectory, for bounding the theoretical context of emergence relative to prospects for elimination, and for framing the larger economic, behavioural and social context that will influence policy decisions and the pathogen’s outcome.

Global potential distribution of three underappreciated arboviruses vectors (Aedes japonicus, Aedes vexans and Aedes vittatus) under current and future climate conditions

Arboviruses (arthropod-borne viruses) are expanding their geographic range, posing significant health threats to millions of people worldwide. This expansion is associated with efficient and suitable vector availability. Apart from the well-known Aedes aegypti and Ae. albopictus, other Aedes species may potentially promote the geographic spread of arboviruses because these viruses have similar vector requirements. Aedes japonicus, Ae. vexans and Ae. vittatus are a growing concern, given their potential and known vector competence for several arboviruses including dengue, chikungunya, and Zika viruses. In the present study, we developed detailed maps of their global potential distributions under both current and future (2050) climate conditions, using an ecological niche modeling approach (Maxent). Under present-day conditions, Ae. japonicus and Ae. vexans have suitable areas in the northeastern United States, across Europe and in southeastern China, whereas the tropical regions of South America, Africa and Asia are more suitable for Ae. vittatus. Future scenarios anticipated range changes for the three species, with each expected to expand into new areas that are currently not suitable. By 2050, Ae. japonicus will have a broader potential distribution across much of Europe, the United States, western Russia and central Asia. Aedes vexans may be able to expand its range, especially in Libya, Egypt and southern Australia. For Ae. vittatus, future projections indicated areas at risk in sub-Saharan Africa and the Middle East. As such, these species deserve as much attention as Ae. aegypti and Ae. albopictus when processing arboviruses risk assessments and our findings may help to better understand the potential distribution of each species.

‘Exploring socioeconomic status as a global determinant of COVID-19 prevalence, using statistical, exploratory data analytic, and supervised machine learning techniques.’ (Preprint)

Background

The COVID-19 pandemic represents the most unprecedented global challenge in recent times. As the global community attempts to manage the pandemic in the long term, it is pivotal to understand what factors drive prevalence rates and to predict the future trajectory of the virus.

Objective

This study had 2 objectives. First, it tested the statistical relationship between socioeconomic status and COVID-19 prevalence. Second, it used machine learning techniques to predict cumulative COVID-19 cases in a multicountry sample of 182 countries. Taken together, these objectives will shed light on socioeconomic status as a global risk factor of the COVID-19 pandemic.

Methods

This research used exploratory data analysis and supervised machine learning methods. Exploratory analysis included variable distribution, variable correlations, and outlier detection. Following this, the following 3 supervised regression techniques were applied: linear regression, random forest, and adaptive boosting (AdaBoost). Results were evaluated using k-fold cross-validation and subsequently compared to analyze algorithmic suitability. The analysis involved 2 models. First, the algorithms were trained to predict 2021 COVID-19 prevalence using only 2020 reported case data. Following this, socioeconomic indicators were added as features and the algorithms were trained again. The Human Development Index (HDI) metrics of life expectancy, mean years of schooling, expected years of schooling, and gross national income were used to approximate socioeconomic status.

Results

All variables correlated positively with the 2021 COVID-19 prevalence, with R² values ranging from 0.55 to 0.85. Using socioeconomic indicators, COVID-19 prevalence was predicted with a reasonable degree of accuracy. Using 2020 reported case rates as a lone predictor to predict 2021 prevalence rates, the average predictive accuracy of the algorithms was low (R²=0.543). When socioeconomic indicators were added alongside 2020 prevalence rates as features, the average predictive performance improved considerably (R²=0.721) and all error statistics decreased. Thus, adding socioeconomic indicators alongside 2020 reported case data optimized the prediction of COVID-19 prevalence to a considerable degree. Linear regression was the strongest learner with R²=0.693 on the first model and R²=0.763 on the second model, followed by random forest (0.481 and 0.722) and AdaBoost (0.454 and 0.679). Following this, the second model was retrained using a selection of additional COVID-19 risk factors (population density, median age, and vaccination uptake) instead of the HDI metrics. However, average accuracy dropped to 0.649, which highlights the value of socioeconomic status as a predictor of COVID-19 cases in the chosen sample.

Conclusions

The results show that socioeconomic status is an important variable to consider in future epidemiological modeling, and highlights the reality of the COVID-19 pandemic as a social phenomenon and a health care phenomenon. This paper also puts forward new considerations about the application of statistical and machine learning techniques to understand and combat the COVID-19 pandemic.

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.

Aedes aegypti mosquitoes affect millions of people each year through infectious diseases such as chikungunya, dengue, and Zika. Because local vector levels need to be sufficiently high for associated outbreaks to occur, the ability to estimate mosquito abundance is a central component of assessing disease risk. The mosquito landscape model (MoLS) is a mechanistic model that estimates Aedes aegypti abundance from local weather time series, and is able to reproduce trends observed in surveillance data. However, scaling this up to a large number of locations is resource intensive, requiring a high-performance computing system. In this article, we develop artificial neural network models that are significantly faster than MoLS and can produce abundance estimates directly from local weather data. This approach reduces the computational time associated with estimating local mosquito levels, thereby allowing for a corresponding increase in the spatiotemporal resolution of these predictions. We compare network design choices, including architecture and training data, in their ability to accurately reproduce MoLS estimates and analyze model performance in locations across the contiguous United States.

Zika virus infection and microcephaly: spatial analysis and socio-environmental determinants in a region of high Aedes aegypti infestation in the Central-West Region of Brazil

Background

More than 5 years after the Zika virus (ZIKV) epidemic, Zika infection remains a major concern in regions with high Aedes infestation. The objectives of this study were (i) to identify clusters of ZIKV infection and microcephaly, and/or central nervous system (CNS) alterations associated with congenital infection during the epidemic peak in 2016 and subsequently, in 2017 and 2018; (ii) to measure the non-spatial correlation between ZIKV infection and microcephaly and/or CNS alterations associated with congenital infection; and (iii) to analyse the sociodemographic/economic, health, and environmental determinants associated with the incidence of ZIKV in a region of high infestation by Aedes aegypti in the Central-West Region of Brazil.

Methods

This ecological study analysed 246 municipalities in the state of Goiás (6.9 million inhabitants). The data were obtained from the Information System for Notifiable Diseases (ZIKV cases) and the Public Health Event Registry (microcephaly and/or CNS alterations associated with congenital infection). Incidence rates and prevalence of ZIKA infection were smoothed by an empirical Bayesian estimator (LEbayes), producing the local empirical Bayesian rate (LEBR). In the spatial analysis, ZIKV infection and microcephaly cases were georeferenced by the municipality of residence for 2016 and grouped for 2017 and 2018. Global Moran's I and the Hot Spot Analysis tool (Getis-Ord Gi* statistics) were used to analyse the spatial autocorrelation and clusters of ZIKV infection and microcephaly, respectively. A generalised linear model from the Poisson family was used to assess the association between ecological determinants and the smoothing incidence rate of ZIKV infection.

Results

A total of 9892 cases of acute ZIKV infection and 121 cases of microcephaly were confirmed. The mean LEBR of the ZIKV infection in the 246 municipalities was 22.3 cases/100,000 inhabitants in 2016, and 10.3 cases/100,000 inhabitants in 2017 and 2018. The LEBR of the prevalence rate of microcephaly and/or CNS alterations associated with congenital infection was 7 cases/10,000 live births in 2016 and 2 cases/10,000 live births during 2017–2018. Hotspots of ZIKV infection and microcephaly cases were identified in the capital and neighbouring municipalities in 2016, with new clusters in the following years. In a multiple regression Poisson analysis, ZIKV infection was associated with higher population density, the incidence of dengue, Aedes larvae infestation index, and average rainfall. The important determinant of ZIKV infection incidence reduction was the increase in households attended by endemic disease control agents.

Conclusions

Our analyses were able to capture, in a more granular way, aspects that make it possible to inform public managers of the sentinel areas identified in the post-epidemic hotspots.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06805-1.

A review exploring the overarching burden of Zika virus with emphasis on epidemiological case studies from Brazil

This paper explores the main factors for mosquito-borne transmission of the Zika virus by focusing on environmental, anthropogenic, and social risks. A literature review was conducted bringing together related information from this genre of research from peer-reviewed publications. It was observed that environmental conditions, especially precipitation, humidity, and temperature, played a role in the transmission. Furthermore, anthropogenic factors including sanitation, urbanization, and environmental pollution promote the transmission by affecting the mosquito density. In addition, socioeconomic factors such as poverty as well as social inequality and low-quality housing have also an impact since these are social factors that limit access to certain facilities or infrastructure which, in turn, promote transmission when absent (e.g., piped water and screened windows). Finally, the paper presents short-, mid-, and long-term preventative solutions together with future perspectives. This is the first review exploring the effects of anthropogenic aspects on Zika transmission with a special emphasis in Brazil.

Geospatial Analysis and Mapping Strategies for Fine-Grained and Detailed COVID-19 Data with GIS

The unprecedented COVID-19 pandemic is showing dramatic impact across the world. Public health authorities attempt to fight against the virus while maintaining economic activity. In the face of the uncertainty derived from the virus, all the countries have adopted non-pharmaceutical interventions for limiting the mobility and maintaining social distancing. In order to support these interventions, some health authorities and governments have opted for sharing very fine-grained data related with the impact of the virus in their territories. Geographical science is playing a major role in terms of understanding how the virus spreads across regions. Location of cases allows identifying the spatial patterns traced by the virus. Understanding these patterns makes controlling the virus spread feasible, minimizes its impact in vulnerable regions, anticipates potential outbreaks, or elaborates predictive risk maps. The application of geospatial analysis to fine-grained data must be urgently adopted for optimal decision making in real and near-real time. However, some aspects related to process and map sensitive health data in emergency cases have not yet been sufficiently explored. Among them include concerns about how these datasets with sensitive information must be shown depending on aspects related to data aggregation, scaling, privacy issues, or the need to know in advance the particularities of the study area. In this paper, we introduce our experience in mapping fine-grained data related to the incidence of the COVID-19 during the first wave in the region of Galicia (NW Spain), and after that we discuss the mentioned aspects.

Epidemiological Characteristics and Regional Risk Prediction of Hemorrhagic Fever with Renal Syndrome in Shandong Province, China

BACKGROUND

Hemorrhagic fever with renal syndrome (HFRS), a rodent-borne disease caused by different species of hantaviruses, is widely endemic in China. Shandong Province is one of the most affected areas. This study aims to analyze the epidemiological characteristics of HFRS, and to predict the regional risk in Shandong Province.

METHODS

Descriptive statistics were used to elucidate the epidemiological characteristics of HFRS cases in Shandong Province from 2010 to 2018. Based on environmental and socioeconomic data, the boosted regression tree (BRT) model was applied to identify important influencing factors, as well as predict the infection risk zones of HFRS.

RESULTS

A total of 11,432 HFRS cases were reported from 2010 to 2018 in Shandong, with groups aged 31-70 years (81.04%), and farmers (84.44%) being the majority. Most cases were from central and southeast Shandong. There were two incidence peak periods in April to June and October to December, respectively. According to the BRT model, we found that population density (a relative contribution of 15.90%), elevation (12.02%), grassland (11.06%), cultivated land (9.98%), rural settlement (9.25%), woodland (8.71%), and water body (8.63%) were relatively important influencing factors for HFRS epidemics, and the predicted high infection risk areas were concentrated in central and eastern areas of Shandong Province. The BRT model provided an overall prediction accuracy, with an area under the receiver operating characteristic curve of 0.91 (range: 0.83-0.95).

CONCLUSIONS

HFRS in Shandong Province has shown seasonal and spatial clustering characteristics. Middle-aged and elderly farmers are a high-risk population. The BRT model has satisfactory predictive capability in stratifying the regional risk of HFRS at a county level in Shandong Province, which could serve as an important tool for risk assessment of HFRS to deploy prevention and control measures.

Current development of Zika virus vaccines with special emphasis on virus-like particle technology

Introduction: Zika virus disease received little attention until its recent explosive emergence around the globe. The devastating consequences of this pandemic include congenital Zika syndrome (CZS) and the neurological autoimmune disorder Guillain-Barré syndrome. These potential outcomes prompted massive efforts to understand the course of Zika infection and to develop therapeutic and prophylactic strategies for treatment and prevention of disease.Area covered: Preclinical and clinical data demonstrate that a safe and efficacious vaccine for protection against Zika virus infection is possible in the near future. Nevertheless, significant knowledge gaps regarding the outcome of a mass vaccination strategy exist and must be addressed. Zika virus circulates in flavivirus-endemic regions, an ideal Zika vaccine should avoid the potential of antibody-dependent enhancement from exposure to dengue virus. Prevention of CZS is the primary goal for immunization, and the vaccine must provide protection against intrauterine transmission for use during pregnancy and in women of childbearing age. Ideally, a vaccine should also prevent sexual transmission of the virus through mucosal protection.Expert opinion: This review describes current vaccine approaches against Zika virus with particular attention to the application of virus-like particle (VLP) technology as a strategy for solving the challenges of Zika virus immunization.

Transmission of SARS-CoV-2 virus and ambient temperature: a critical review

The coronavirus disease 2019 (COVID-19) pandemic has brought unprecedented public health, and social and economic challenges. It remains unclear whether seasonal changes in ambient temperature will alter spreading trajectory of the COVID-19 epidemic. The probable mechanism on this is still lacking. This review summarizes the most recent research data on the effect of ambient temperature on the COVID-19 epidemic characteristic. The available data suggest that (i) mesophilic traits of viruses are different due to their molecular composition; (ii) increasing ambient temperature decreases the persistence of some viruses in aquatic media; (iii) a 1°C increase in the average monthly minimum ambient temperatures (AMMAT) was related to a 0.72% fewer mammalian individuals that would be infected by coronavirus; (iv) proportion of zoonotic viruses of mammals including humans is probably related to their body temperature difference; (v) seasonal divergence between the northern and southern hemispheres may be a significant driver in determining a waved trajectory in the next 2 years. Further research is needed to understand its effects and mechanisms of global temperature change so that effective strategies can be adopted to curb its natural effects. This paper mainly explores possible scientific hypothesis and evidences that local communities and authorities should consider to find optimal solutions that can limit the transmission of SARS-CoV-2 virus.

Approaches and perspectives for online learning during the COVID-19 pandemic and future chaos

BACKGROUND

Online learning (OLL) methodology has been incorporated in higher education extensively on the mount over the last few decades and with the onset of COVID-19 situation, the virtual method in academia became essential. After observing worldly destructions and death due to coronavirus, the WHO declared a high alert emergency, and since Pakistan started to follow lockdown since March 2020 to prevent high penetration and consequently, the online teaching method was adapted to keep the learning atmosphere alive. The study is aimed to identify the influencing factors and compliance for the implementation of the OLL system in COVID-19 and, in the future, nonpandemic state as well.

MATERIALS AND METHODS

The present study is designed to observe the adaptability and hurdles among pharmacy students, and for the purpose of the study, a questionnaire on the Likert scale was developed and asked to respond from pharmacy undergraduate and postgraduate students after taking consensus, which further analyzed through IBM SPSS Statistics Version 26.

RESULTS

The outcomes of the study were analyzed with a high level of conformity; however, the gender-based reservations were observed, and moreover, merits of OLL were observed with high jeopardy.

CONCLUSION

The adaptation of OLL, though proved as the only troubleshooter in an uncontrollable situation and enabled universities, professors, and students for patience, resilience, and hopefully, will serve as swift safeguard for future challenges of epidemic and pandemic disasters. According to the evolution theory, brain adaptability and plasticity of human organisms grow the capacity to adjust itself with an advanced characteristic.