Exposure of small ruminants to the Schmallenberg arbovirus in Germany from 2017 to 2018 - animal-specific and flock-management-related risk factors.

The Schmallenberg virus (SBV), an emerging Orthobunyavirus of mainly ruminant hosts, caused a substantial epidemic in European ruminant populations between 2011 and 2013. The pathogen is transmitted by arthropod vectors (Culicoides spp.) and can cause reproductive disorders and severe malformations of the offspring or stillbirth. The present study aimed to assess SBV seroprevalence among German sheep and goats a few years after the first virus detection in the country (November 2011). In addition, an extensive risk factor analysis including host-specific and husbandry-related factors was implemented. Seroprevalence was determined by examining serum samples from 2759 sheep and 446 goats out of a total of 70 flocks across five German federal states. The samples were withdrawn in the period between 2017 and 2018. Using a commercial competitive ELISA, antibodies against SBV were detected in all 70 investigated flocks. A percentage of 60.1 % (1657/2759) of the sheep and 40.4 % (180/446) of the goat sera contained SBV antibodies. Generalized linear mixed modeling revealed significant effects of host species (sheep > goats), age (old > young) and sex (female > male) on SBV seroprevalence. For both species, also the farming purpose, and for goats, ectoparasite treatment and the presence of cattle on the farm played a role in terms of risk for SBV exposure. The observations from this study still emphasize a wide distribution of the pathogen in Germany. Nevertheless, the observed seroprevalence might not be sufficient to achieve effective herd immunity. Pinpointing risk factors identified susceptible populations for targeted vaccination programs to reduce potential animal losses caused by SBV.

Effectiveness of Vaccination Against Tick-Borne Encephalitis in the Czech Republic, 2018-2022.

Background: Tick-borne encephalitis (TBE) is caused by the tick-borne encephalitis virus (TBEV). TBEV infection can cause symptoms of central nervous system (CNS) inflammation and result in severe consequences including death. TBE is an increasing health threat in the Czech Republic and elsewhere in Europe. In 2020, 23% of 3734 TBE cases reported to the European Centre for Disease Prevention and Control were from the Czech Republic. TBE vaccination is universally recommended in the Czech Republic, but a full analysis of TBE vaccine effectiveness (VE) in the Czech Republic has not been published. Methods: TBE is a notifiable disease in the Czech Republic with mandatory reporting of cases (i.e., laboratory-confirmed TBEV infected patient with symptoms of CNS inflammation) and vaccination history to public health authorities. TBE VE was estimated using the screening method utilizing public health surveillance data from 2018 to 2022 and online household surveys of the general population on TBE vaccine uptake conducted in 2019-2022. Results: In 2018-2022, 3648 TBE cases were reported in the Czech Republic; 98.1% (3105/3166) of TBE cases with known vaccination history were unvaccinated. Among 42,671 persons surveyed from the general population who had known TBE vaccination history, 66.5% were unvaccinated. VE against TBE was 97.6% (95% confidence interval 95.7-98.7). When stratified by age group, VE was 97.1% (88.4-99.3) in 1-15 years of age, 97.9% (95.3-99.0) in 16-59 years of age, and 96.9% (90.5-99.0) in ≥60 years of age. TBE vaccination averted an estimated 1020 TBE cases in the Czech Republic from 2018 to 2022. Conclusions: This first published study with a full analysis of TBE VE in the Czech Republic showed that vaccination was highly effective for the prevention of TBE including in children, an age group with increasing TBE disease burden. Vaccination averted hundreds of TBE cases and hospitalizations despite the relatively low compliance with TBE vaccine recommendations. To prevent additional TBE cases in the Czech Republic, enhanced efforts to increase TBE vaccine uptake are needed.

Detection of Wuchereria bancrofti infection in mosquitoes in areas co-endemic with Brugia malayi in Balasore district, Odisha, India.

Lymphatic filariasis (LF) is a crippling and disfiguring parasitic condition. India accounts for 55% of the world's LF burden. The filarial parasite Wuchereria bancrofti is known to cause 99.4% of the cases while, Brugia malayi accounts for 0.6% of the issue occurring mainly in some pockets of Odisha and Kerala states. The Balasore (Baleswar) district of Odisha has been a known focus of B. malayi transmission. We employed molecular xenomonitoring to detect filarial parasite DNA in vectors. In six selected villages, Gravid traps were used to collect Culex mosquitoes and hand catch method using aspirators was followed for collection of mansonioides. A total of 2903 mosquitoes comprising of Cx. quinquefasciatus (n = 2611; 89.94%), Cx. tritaeniorhynchus (n = 100; 3.44%), Mansonia annuliferea (n = 139; 4.78%) and Mansonia uniformis (n = 53; 1.82%) were collected from six endemic villages. The species wise mosquitoes were made into 118 pools, each with a maximum of 25 mosquitoes, dried and transported to the laboratory at VCRC, Puducherry. The mosquito pools were subjected to parasite DNA extraction, followed by Real-time PCR using LDR and HhaI probes to detect W. bancrofti and B. malayi infections, respectively. Seven pools (6.66%) of Cx. quinquefasciatus, showed infection with only W. bancrofti while none of the pools of other mosquito species showed infection with either W. bancrofti or B. malayi. Although the study area is endemic to B. malayi, none of the vectors of B. malayi was found with parasite infection. This study highlights the ongoing transmission of bancroftian filariasis in the study villages of Balasore district of Odisha and its implications for evaluating LF elimination programme.

Seasonal variability and stochastic branching process in malaria outbreak probability.

BACKGROUND: Malaria is the world's most fatal and challenging parasitic disease, caused by the Plasmodium parasite, which is transmitted to humans by the bites of infected female mosquitoes. Bangladesh is the most vulnerable region to spread malaria because of its geographic position. In this paper, we have considered the dynamics of vector-host models and observed the stochastic behavior. This study elaborates on the seasonal variability and calculates the probability of disease outbreaks. METHODS: We present a model for malaria disease transmission and develop its corresponding continuous-time Markov chain (CTMC) representation. The proposed vector-host models illustrate the malaria transmission model along with sensitivity analysis. The deterministic model with CTMC curves is depicted to show the randomness in real scenarios. Sequentially, we expand these studies to a time-varying stochastic vector-host model that incorporates seasonal variability. Phase plane analysis is conducted to explore the characteristics of the disease, examine interactions among various compartments, and evaluate the impact of key parameters. The branching process approximation is developed for the corresponding vector-host model to calculate the probability outbreak. Numerous numerical results are accomplished to observe the analytical investigation. RESULTS: Seasonality and contact patterns affect the dynamics of disease outbreaks. The numerical illustration provides that the probability of a disease outbreak depends on the infected host or vector. Additionally, periodic transmission rates have a great influence on the probability outbreak. The basic reproduction number (R0) is derived, which is the main justification for studying the dynamical behavior of epidemic models. CONCLUSIONS: Seasonal variability significantly impacts malaria transmission, and the probability of disease outbreaks is influenced by time and the initial number of infected individuals. Moreover, the branching process approximation is applicable when the population size is large enough and the basic reproduction number is less than 1. In the future, such analysis can help decision-makers understand the impact of various parameters and their stochastic behavior in the vector-host model to prevent such types of disease outbreaks.

Association between Thoracic Radiographic Changes and Indicators of Pulmonary Hypertension in Dogs with Heartworm Disease.

Pulmonary hypertension (PH) is a consequence of pulmonary endarteritis during infection with Dirofilaria immitis in dogs. Echocardiography is the technique of choice but is not always accessible to all clinicians. This study aimed to evaluate the association of the radiological findings in dogs with heartworm disease and the presence or absence of echocardiographically characterised PH. The study included 62 heartworm-infected dogs that underwent thoracic radiographs and echocardiography. The studied dogs showed moderate to severe PH when the Right Pulmonary Artery Distensibility (RPAD) Index was <29.5%. The RPAD Index was used for comparison with thoracic radiographs. The Vertebral Heart Size (VHS), right cranial pulmonary artery passing through the fourth rib in the laterolateral projection (CrPA/R4) ratio, and right caudal pulmonary artery to the ninth rib in the dorsoventral projection (CdPA/R9) ratio showed significant differences between dogs with/without PH (p < 0.001). Sensitivity (sen) and specificity (sp) cut-off values were obtained: VHS ≥ 9.53 (sen 93.75%, sp 63.33%); CrPA/R4 ≥ 1.08 (sen 87.5%, sp 70%); and CdPA/R9 ≥ 1.10 (sen 96.88%, sp 76.66%). The CrPA/R4 and CdPA/R9 ratios showed potential as a preliminary screening tool for PH in heartworm-infected dogs, suggesting that they may reliably indicate the presence of PH and guide the decision for further diagnostic testing.

bistro: An R package for vector bloodmeal identification by short tandem repeat overlap.

Measuring vector-human contact in a natural setting can inform precise targeting of interventions to interrupt transmission of vector-borne diseases. One approach is to directly match human DNA in vector bloodmeals to the individuals who were bitten using genotype panels of discriminative short tandem repeats (STRs). Existing methods for matching STR profiles in bloodmeals to the people bitten preclude the ability to match most incomplete profiles and multi-source bloodmeals to bitten individuals.We developed bistro, an R package that implements 3 preexisting STR matching methods as well as the package's namesake, bistro, a new algorithm described here. bistro employs forensic analysis methods to calculate likelihood ratios and match human STR profiles in bloodmeals to people using a dynamic threshold. We evaluated the algorithm's accuracy and compared it to existing matching approaches using a publicly-available panel of 188 single-source and 100 multi-source samples containing DNA from 50 known human sources. Then we applied it to match 777 newly field-collected mosquito bloodmeals to a database of 645 people.The R package implements four STR matching algorithms in user-friendly functions with clear documentation. bistro correctly matched 99% (187/188) of profiles in single-source samples, and 62% (224/359) of profiles from multi-source samples, resulting in a sensitivity of 0.75 (vs < 0.51 for other algorithms). The specificity of bistro was 0.9998 (vs. 1 for other algorithms). Furthermore, bistro identified 79% (720/906) of all possible matches for field-derived mosquitoes, yielding 1.4x more matches than existing algorithms.bistro identifies more correct bloodmeal-human matches than existing approaches, enabling more accurate and robust analyses of vector-human contact in natural settings. The bistro R package and corresponding documentation allow for straightforward uptake of this algorithm by others.

Forecasting deep learning-based risk assessment of vector-borne diseases using hybrid methodology.

BACKGROUND: Dengue fever is rapidly becoming Malaysia's most pressing health concern, as the reported cases have nearly doubled over the past decade. Without efficacious antiviral medications, vector control remains the primary strategy for battling dengue, while the recently introduced tetravalent immunization is being evaluated. The most significant and dangerous risk increasing recently is vector-borne illnesses. These illnesses induce significant human sickness and are transmitted by blood-feeding arthropods such as fleas, parasites, and mosquitos. A thorough grasp of various factors is necessary to improve prediction accuracy and typically generate inaccurate and unstable predictions, as well as machine learning (ML) models, weather-driven mechanisms, and numerical time series. OBJECTIVE: In this research, we propose a novel method for forecasting vector-borne disease risk using Radial Basis Function Networks (RBFNs) and the Darts Game Optimizer (DGO) algorithm. METHODS: The proposed approach entails training the RBFNs with historical disease data and enhancing their parameters with the DGO algorithm. To prepare the RBFNs, we used a massive dataset of vector-borne disease incidences, climate variables, and geographical data. The DGO algorithm proficiently searches the RBFN parameter space, fine-tuning the model's architecture to increase forecast accuracy. RESULTS: RBFN-DGO provides a potential method for predicting vector-borne disease risk. This study advances predictive demonstrating in public health by shedding light on effectively controlling vector-borne diseases to protect human populations. We conducted extensive testing to evaluate the performance of the proposed method to standard optimization methods and alternative forecasting methods. CONCLUSION: According to the findings, the RBFN-DGO model beats others in terms of accuracy and robustness in predicting the likelihood of vector-borne illness occurrences.

Infectivity of Plasmodium parasites to Aedes aegypti and Anopheles stephensi mosquitoes maintained on blood-free meals of SkitoSnack.

BACKGROUND: Aedes and Anopheles mosquitoes are responsible for tremendous global health burdens from their transmission of pathogens causing malaria, lymphatic filariasis, dengue, and yellow fever. Innovative vector control strategies will help to reduce the prevalence of these diseases. Mass rearing of mosquitoes for research and support of these strategies presently depends on meals of vertebrate blood, which is subject to acquisition, handling, and storage issues. Various blood-free replacements have been formulated for these mosquitoes, but none of these replacements are in wide use, and little is known about their potential impact on competence of the mosquitoes for Plasmodium infection. METHODS: Colonies of Aedes aegypti and Anopheles stephensi were continuously maintained on a blood-free replacement (SkitoSnack; SS) or bovine blood (BB) and monitored for engorgement and hatch rates. Infections of Ae. aegypti and An. stephensi were assessed with Plasmodium gallinaceum and P. falciparum, respectively. RESULTS: Replicate colonies of mosquitoes were maintained on BB or SS for 10 generations of Ae. aegypti and more than 63 generations of An. stephensi. The odds of engorgement by SS- relative to BB-maintained mosquitoes were higher for both Ae. aegypti (OR = 2.6, 95% CI 1.3-5.2) and An. stephensi (OR 2.7, 95% CI 1.4-5.5), while lower odds of hatching were found for eggs from the SS-maintained mosquitoes of both species (Ae. aegypti OR = 0.40, 95% CI 0.26-0.62; An. stephensi OR = 0.59, 95% CI 0.36-0.96). Oocyst counts were similar for P. gallinaceum infections of Ae. aegypti mosquitoes maintained on SS or BB (mean ratio = [mean on SS]/[mean on BB] = 1.11, 95% CI 0.85-1.49). Similar oocyst counts were also observed from the P. falciparum infections of SS- or BB-maintained An. stephensi (mean ratio = 0.76, 95% CI 0.44-1.37). The average counts of sporozoites/mosquito showed no evidence of reductions in the SS-maintained relative to BB-maintained mosquitoes of both species. CONCLUSIONS: Aedes aegypti and An. stephensi can be reliably maintained on SS over multiple generations and are as competent for Plasmodium infection as mosquitoes maintained on BB. Use of SS alleviates the need to acquire and preserve blood for mosquito husbandry and may support new initiatives in fundamental and applied research, including novel manipulations of midgut microbiota and factors important to the mosquito life cycle and pathogen susceptibility.

Uncovering the genetic diversity in Aedes aegypti insecticide resistance genes through global comparative genomics.

Aedes aegypti is vector of many arboviruses including Zika, dengue, yellow fever, West Nile, and Chikungunya. Its control efforts are hampered by widespread insecticide resistance reported in the Americas and Asia, while data from Africa is more limited. Here we use publicly available 729 Ae. aegypti whole-genome sequencing samples from 15 countries, including nine in Africa, to investigate the genetic diversity in four insecticide resistance linked genes: ace-1, GSTe2, rdl and vgsc. Apart from vgsc, the other genes have been less investigated in Ae. aegypti, and almost no genetic diversity information is available. Among the four genes, we identified 1,829 genetic variants including 474 non-synonymous substitutions, some of which have been previously documented, as well as putative copy number variations in GSTe2 and vgsc. Global insecticide resistance phenotypic data demonstrated variable resistance in geographic areas with resistant genotypes. Overall, our work provides the first global catalogue and geographic distribution of known and new amino-acid mutations and duplications that can be used to guide the identification of resistance drivers in Ae. aegypti and thereby support monitoring efforts and strategies for vector control.

Molecular detection of Setaria tundra (Issaitshikoff & Rajewskaya, 1928) and Setaria cervi (Rudolphi, 1819) in red deer in Slovakia.

Filaroid nematodes Setaria tundra (Issaitshikoff & Rajewskaya, 1928) and Setaria cervi (Rudolphi, 1819) are internal parasites from family Onchocercidae with occurrence in the northern hemisphere. They have a considerably wide range of final host, including many species of family Cervidae. Intermediate hosts and vectors at the same time, are represented by the several mosquito species, mostly of genus Aedes. Infection of Setaria is relatively harmless and especially in wild cervids usually pass unnoticed. Although in some cases it can induce peritonitis which might be a life threatening condition.This study was determined to reveal the presence of helminths Setaria tundra and Setaria cervi in red deer (Cervus elaphus) in Slovakia. The parasites were identified morphologically and genetically, based on the sequences of a fragment of the mitochondrial cytochrome c oxidase subunit 1 (cox1) gene. For this purpose we used partial results of our longer parasitological monitoring realized in one particular hunting area located in eastern Slovakia, near the city of Kosice. A total of 60 red deer individuals were tested, of which one was found to be infected with Setaria tundra (prevalence of 1.7%) and four were detected to be infected with Setaria cervi (prevalence 6.7%). The intensity of infection was very low, only one specimen of Setaria spp. in each positive animal.

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.

Comparative reservoir competence of Peromyscus leucopus, C57BL/6J, and C3H/HeN for Borrelia burgdorferi B31.

Borrelia burgdorferi, a Lyme disease spirochete, causes a range of acute and chronic maladies in humans. However, a primary vertebrate reservoir in the United States, the white-footed deermouse Peromyscus leucopus, is reported not to have reduced fitness following infection. Although laboratory strains of Mus musculus mice have successfully been leveraged to model acute human Lyme disease, the ability of these rodents to model B. burgdorferi-P. leucopus interactions remains understudied. Here, we compared infection of P. leucopus with B. burgdorferi B31 with infection of the traditional B. burgdorferi murine models-C57BL/6J and C3H/HeN Mus musculus, which develop signs of inflammation akin to human disease. We find that B. burgdorferi was able to reach much higher burdens (10- to 30-times higher) in multiple M. musculus skin sites and that the overall dynamics of infection differed between the two rodent species. We also found that P. leucopus remained transmissive to larval Ixodes scapularis for a far shorter period than either M. musculus strain. In line with these observations, we found that P. leucopus does launch a modest but sustained inflammatory response against B. burgdorferi in the skin, which we hypothesize leads to reduced bacterial viability and rodent-to-tick transmission in these hosts. Similarly, we also observe evidence of inflammation in infected P. leucopus hearts. These observations provide new insight into reservoir species and the B. burgdorferi enzootic cycle.IMPORTANCEA Lyme disease-causing bacteria, Borrelia burgdorferi, must alternate between infecting a vertebrate host-usually rodents or birds-and ticks. In order to be successful in that endeavor, the bacteria must avoid being killed by the vertebrate host before it can infect a new larval tick. In this work, we examine how B. burgdorferi and one of its primary vertebrate reservoirs, Peromyscus leucopus, interact during an experimental infection. We find that B. burgdorferi appears to colonize its natural host less successfully than conventional laboratory mouse models, which aligns with a sustained seemingly anti-bacterial response by P. leucopus against the microbe. These data enhance our understanding of P. leucopus host-pathogen interactions and could potentially serve as a foundation to uncover ways to disrupt the spread of B. burgdorferi in nature.

An atlas of human vector-borne microbe interactions reveals pathogenicity mechanisms.

Vector-borne diseases are a leading cause of death worldwide and pose a substantial unmet medical need. Pathogens binding to host extracellular proteins (the "exoproteome") represents a crucial interface in the etiology of vector-borne disease. Here, we used bacterial selection to elucidate host-microbe interactions in high throughput (BASEHIT)-a technique enabling interrogation of microbial interactions with 3,324 human exoproteins-to profile the interactomes of 82 human-pathogen samples, including 30 strains of arthropod-borne pathogens and 8 strains of related non-vector-borne pathogens. The resulting atlas revealed 1,303 putative interactions, including hundreds of pairings with potential roles in pathogenesis, including cell invasion, tissue colonization, immune evasion, and host sensing. Subsequent functional investigations uncovered that Lyme disease spirochetes recognize epidermal growth factor as an environmental cue of transcriptional regulation and that conserved interactions between intracellular pathogens and thioredoxins facilitate cell invasion. In summary, this interactome atlas provides molecular-level insights into microbial pathogenesis and reveals potential host-directed targets for next-generation therapeutics.

Epidemiological Trends of Dengue in One of the Western Hilly Areas of Himachal Pradesh.

Background: Dengue is one of the neglected tropical diseases, with a wide spectrum of diseases, ranging from acute febrile illness dengue fever to life-threatening dengue hemorrhagic fever or dengue shock syndrome. In recent years, it has become a major public health concern in many nonendemic areas as well. Materials and Methods: A secondary data analysis of records available with district Integrated Disease Surveillance Programme cell was conducted to study distribution (time, place, and person) of dengue from 2017 to 2022 in Kangra, a sub-Himalayan district of Himachal Pradesh (HP). Results: In the evaluated period (2017-2022), a total of 6008 cases suspected of dengue were tested and test positivity of 7% (441) with male gender predominance was found. Mean age of the diagnosed cases was 37.7 ± 16.8 years. A seasonal trend was observed starting from late August to November in all study years. Conclusion: Dengue is still a neglected disease, but it has shown its presence especially in this part of HP, indicating the need for better preparation and sensitization of vector-borne disease control program activities, especially in post-monsoon, to prevent future epidemics.

Abstruse Side of Climate Change, Impact on Malaria: A Systematic Evidence Review Comparing Iran versus Globally.

Background: Infectious outbreaks due to disrupted social and environmental conditions after climate change-induced events complicate disasters. This research aimed to determine the contentions of bioclimatic variables and extreme events on the prevalence of the most common Climate-Sensitive Infectious Disease (CSID); Malaria in Iran. Methods: The present narrative systematic review study was conducted on the bioclimatic variable impact on the prevalence of malaria, as a common CSID. The search was conducted in 3 sections: global climate change-related studies, disaster related, and studies that were conducted in Iran. The literature search was focused on papers published in English and Persian from Mar 2000 to Dec 2021, using electronic databases; Scopus, Web of Science, PubMed, Google Scholar, SID, Magiran, and IranDoc. Results: Overall, 41 studies met the inclusion criteria. The various types of climatic variables including; Temperature, rainfall, relative humidity, and hydrological events including; flood, drought, and cyclones has been reported as a predictor of malaria. The results of studies, inappropriately and often were inconsistent in both Iran and other parts of the world. Conclusion: Identifying malaria outbreak risks is essential to assess vulnerability, and a starting point to identify where the health system is required to reduce the vulnerability and exposure of the population. The finding of most related studies is not congruent to achieve reliable information, more extensive studies in all climates and regions of the country, by climatic models and high accuracy risk map, using the long period of bioclimatic variables and malaria trend is recommended.

Serosurveillance of dengue infection and correlation with mosquito pools for dengue virus positivity during the COVID-19 pandemic in Tamil Nadu, India - A state-wide cross-sectional cluster randomized community-based study.

Background: Dengue is a vector-borne viral disease impacting millions across the globe. Nevertheless, akin to many other diseases, reports indicated a decline in dengue incidence and seroprevalence during the COVID-19 pandemic (2020-22). This presumably could be attributed to reduced treatment-seeking rates, under-reporting, misdiagnosis, disrupted health services and reduced exposure to vectors due to lockdowns. Scientific evidence on dengue virus (DENV) disease during the COVID-19 pandemic is limited globally. Methods: A cross-sectional, randomized cluster sampling community-based survey was carried out to assess anti-dengue IgM and IgG and SARS-CoV-2 IgG seroprevalence across all 38 districts of Tamil Nadu, India. The prevalence of DENV in the Aedes mosquito pools during 2021 was analyzed and compared with previous and following years of vector surveillance for DENV by real-time PCR. Findings: Results implicate that both DENV-IgM and IgG seroprevalence and mosquito viral positivity were reduced across all the districts. A total of 13464 mosquito pools and 5577 human serum samples from 186 clusters were collected. Of these, 3·76% of mosquito pools were positive for DENV. In the human sera, 4·12% were positive for DENV IgM and 6·4% were positive for DENV IgG. The anti-SARS-CoV-2 antibody titres correlated with dengue seropositivity with a significant association whereas vaccination status significantly correlated with dengue IgM levels. Interpretation: Continuous monitoring of DENV seroprevalence, especially with the evolving variants of the SARS-CoV-2 virus and surge in COVID-19 cases will shed light on the transmission and therapeutic attributes of dengue infection.

Metatranscriptomic investigation of single Ixodes pacificus ticks reveals diverse microbes, viruses, and novel mRNA-like endogenous viral elements.

Ticks are increasingly important vectors of human and agricultural diseases. While many studies have focused on tick-borne bacteria, far less is known about tick-associated viruses and their roles in public health or tick physiology. To address this, we investigated patterns of bacterial and viral communities across two field populations of western black-legged ticks (Ixodes pacificus). Through metatranscriptomic analysis of 100 individual ticks, we quantified taxon prevalence, abundance, and co-occurrence with other members of the tick microbiome. In addition to commonly found tick-associated microbes, we assembled 11 novel RNA virus genomes from Rhabdoviridae, Chuviridae, Picornaviridae, Phenuiviridae, Reoviridae, Solemovidiae, Narnaviridae and two highly divergent RNA virus genomes lacking sequence similarity to any known viral families. We experimentally verified the presence of these in I. pacificus ticks across several life stages. We also unexpectedly identified numerous virus-like transcripts that are likely encoded by tick genomic DNA, and which are distinct from known endogenous viral element-mediated immunity pathways in invertebrates. Taken together, our work reveals that I. pacificus ticks carry a greater diversity of viruses than previously appreciated, in some cases resulting in evolutionarily acquired virus-like transcripts. Our findings highlight how pervasive and intimate tick-virus interactions are, with major implications for both the fundamental biology and vectorial capacity of I. pacificus ticks. IMPORTANCE: Ticks are increasingly important vectors of disease, particularly in the United States where expanding tick ranges and intrusion into previously wild areas has resulted in increasing human exposure to ticks. Emerging human pathogens have been identified in ticks at an increasing rate, and yet little is known about the full community of microbes circulating in various tick species, a crucial first step to understanding how they interact with each and their tick host, as well as their ability to cause disease in humans. We investigated the bacterial and viral communities of the Western blacklegged tick in California and found 11 previously uncharacterized viruses circulating in this population.

Spatiotemporal models of dengue epidemiology in the Philippines: Integrating remote sensing and interpretable machine learning.

Previous dengue epidemiological analyses have been limited in spatiotemporal extent or covariate dimensions, the latter neglecting the multifactorial nature of dengue. These constraints, caused by rigid and traditional statistical tools which collapse amidst 'Big Data', prompt interpretable machine-learning (iML) approaches. Predicting dengue incidence and mortality in the Philippines, a data-limited yet high-burden country, the mlr3 universe of R packages was used to build and optimize ML models based on remotely sensed provincial and dekadal 3 NDVI and 9 rainfall features from 2016 to 2020. Between two tasks, models differ across four random forest-based learners and two clustering strategies. Among 16 candidates, rfsrc-year-case and ranger-year-death significantly perform best for predicting dengue incidence and mortality, respectively. Therefore, temporal clustering yields the best models, reflective of dengue seasonality. The two best models were subjected to tripartite global exploratory model analyses, which encompass model-agnostic post-hoc methods such as Permutation Feature Importance (PFI) and Accumulated Local Effects (ALE). PFI reveals that the models differ in their important explanatory aspect, rainfall for rfsrc-year-case and NDVI for ranger-year-death, among which long-term average (lta) features are most relevant. Trend-wise, ALE reveals that average incidence predictions are positively associated with 'Rain.lta', reflective of dengue cases peaking during the wet season. In contrast, those for mortality are negatively associated with 'NDVI.lta', reflective of urban spaces driving dengue-related deaths. By technologically addressing the challenges of the human-animal-ecosystem interface, this study adheres to the One Digital Health paradigm operationalized under Sustainable Development Goals (SDGs). Leveraging data digitization and predictive modeling for epidemiological research paves SDG 3, which prioritizes holistic health and well-being.