Heterogeneities in the transmission of infectious agents: implications for the design of control programs

Plasmodium falciparum infection in humans and mosquitoes influence natural Anopheline biting behavior and transmission

The human infectious reservoir of Plasmodium falciparum is governed by transmission efficiency during vector-human contact and mosquito biting preferences. Understanding biting bias in a natural setting can help target interventions to interrupt transmission. In a 15-month cohort in western Kenya, we detected P. falciparum in indoor-resting Anopheles and human blood samples by qPCR and matched mosquito bloodmeals to cohort participants using short-tandem repeat genotyping. Using risk factor analyses and discrete choice models, we assessed mosquito biting behavior with respect to parasite transmission. Biting was highly unequal; 20% of people received 86% of bites. Biting rates were higher on males (biting rate ratio (BRR): 1.68; CI: 1.28-2.19), children 5-15 years (BRR: 1.49; CI: 1.13-1.98), and P. falciparum-infected individuals (BRR: 1.25; CI: 1.01-1.55). In aggregate, P. falciparum-infected school-age (5-15 years) boys accounted for 50% of bites potentially leading to onward transmission and had an entomological inoculation rate 6.4x higher than any other group. Additionally, infectious mosquitoes were nearly 3x more likely than non-infectious mosquitoes to bite P. falciparum-infected individuals (relative risk ratio 2.76, 95% CI 1.65-4.61). Thus, persistent P. falciparum transmission was characterized by disproportionate onward transmission from school-age boys and by the preference of infected mosquitoes to feed upon infected people.

Historical Hot Spots of Dengue and Zika Viruses to Guide Targeted Vector Control in San Juan, Puerto Rico (2010-2022)

Dengue viruses (DENV) continue to cause large outbreaks in tropical countries, while chikungunya and Zika (ZIKV) viruses have added complexity to Aedes-borne disease prevention and control efforts. Because these viruses are transmitted by the same vectors in urban areas, it is useful to understand if sequential outbreaks caused by these viruses have commonalities, such as similar seasonal and spatial patterns, that would help anticipate and perhaps prevent future outbreaks. We explored and analyzed the heterogeneity of confirmed cases of DENV (2010-2014 and 2015-2022) and ZIKV (2016-2017) during outbreaks in the San Juan metropolitan area of Puerto Rico to explore their degree of overlap and prioritize areas for Aedes aegypti control. Deidentified, georeferenced case data were aggregated into grid cells (500 × 500 m) within a geographical information system of the study area and analyzed to calculate the degree of overlap between outbreaks. Spatial autocorrelations using local indicators of spatial associations were conducted to identify significant disease case hot spots and correlations between outbreaks. We found that 75% of cases during the three transmission periods were concentrated in 25% of the total number of grid cells covering the study area. We also found significant clustering of cases during each outbreak, enabling identification of consistent disease hot spots. Our results showed 85% spatial overlap between cases of ZIKV in 2015-2017 and DENV in 2010-2014 and 97% overlap between DENV cases in 2010-2014 and 2015-2022. These results reveal urban areas at greater risk of future arbovirus outbreaks that should be prioritized for vector control.

Behaviour and distribution of Aedes aegypti mosquitoes and their relation to dengue incidence in two transmission hotspots in coastal Ecuador

BACKGROUND

Dengue (DENV) transmission is endemic throughout coastal Ecuador, showing heterogeneous incidence patterns in association with fine-scale variation in Aedes aegypti vector populations and other factors. Here, we investigated the impact of micro-climate and neighbourhood-level variation in urbanization on Aedes abundance, resting behaviour and associations with dengue incidence in two endemic areas.

METHODOLOGY/PRINCIPAL FINDINGS

Aedes aegypti were collected in Quinindé and Portoviejo, two urban cantons with hyperendemic dengue transmission in coastal Ecuador. Aedes vectors were sampled in and around houses within urban and peri-urban neighbourhoods at four time periods. We tested for variation in vector abundance and resting behaviour in relation to neighbourhood urbanization level and microclimatic factors. Aedes abundance increased towards the end of the rainy season, was significantly higher in Portoviejo than in Quinindé, and in urban than in peri-urban neighbourhoods. Aedes vectors were more likely to rest inside houses in Portoviejo but had similar abundance in indoor and outdoor resting collections in Quinindé. Over the study period, DENV incidence was lower in Quinindé than in Portoviejo. Relationships between weekly Ae. aegypti abundance and DENV incidence were highly variable between trapping methods; with positive associations being detected only between BG-sentinel and outdoor Prokopack collections.

CONCLUSIONS/SIGNIFICANCE

Aedes aegypti abundance was significantly higher in urban than peri-urban neighbourhoods, and their resting behaviour varied between study sites. This fine-scale spatial heterogeneity in Ae. aegypti abundance and behaviour could generate site-specific variation in human exposure and the effectiveness of indoor-based interventions. The trap-dependent nature of associations between Aedes abundance and local DENV incidence indicates further work is needed to identify robust entomological indicators of infection risk.

Predicting nodal influence via local iterative metrics

Nodal spreading influence is the capability of a node to activate the rest of the network when it is the seed of spreading. Combining nodal properties (centrality metrics) derived from local and global topological information respectively has been shown to better predict nodal influence than using a single metric. In this work, we investigate to what extent local and global topological information around a node contributes to the prediction of nodal influence and whether relatively local information is sufficient for the prediction. We show that by leveraging the iterative process used to derive a classical nodal centrality such as eigenvector centrality, we can define an iterative metric set that progressively incorporates more global information around the node. We propose to predict nodal influence using an iterative metric set that consists of an iterative metric from order 1 to K produced in an iterative process, encoding gradually more global information as K increases. Three iterative metrics are considered, which converge to three classical node centrality metrics, respectively. In various real-world networks and synthetic networks with community structures, we find that the prediction quality of each iterative based model converges to its optimal when the metric of relatively low orders ( K ∼ 4 ) are included and increases only marginally when further increasing K. This fast convergence of prediction quality with K is further explained by analyzing the correlation between the iterative metric and nodal influence, the convergence rate of each iterative process and network properties. The prediction quality of the best performing iterative metric set with K = 4 is comparable with the benchmark method that combines seven centrality metrics: their prediction quality ratio is within the range [ 91 % , 106 % ] across all three quality measures and networks. In two spatially embedded networks with an extremely large diameter, however, iterative metric of higher orders, thus a large K, is needed to achieve comparable prediction quality with the benchmark.

Economic microbiology: exploring microbes as agents in economic systems

Microbial communities exhibit striking parallels with economic markets, resembling intricate ecosystems where microorganisms engage in resource exchange akin to human market transactions. This dynamic network of resource swapping mirrors economic trade in human markets, with microbes specializing in metabolic functions much like businesses specializing in goods and services. Cooperation and competition are central dynamics in microbial communities, with alliances forming for mutual benefit and species vying for dominance, similar to businesses seeking market share. The human microbiome, comprising trillions of microorganisms within and on our bodies, is not only a marker of socioeconomic status but also a critical factor contributing to persistent health inequalities. Social and economic factors shape the composition of the gut microbiota, impacting healthcare access and quality of life. Moreover, these microbes exert indirect influence over human decisions by affecting neurotransmitter production, influencing mood, behavior, and choices related to diet and emotions. Human activities significantly impact microbial communities, from dietary choices and antibiotic use to environmental changes, disrupting these ecosystems. Beyond their natural roles, humans harness microbial communities for various applications, manipulating their interactions and resource exchanges to achieve specific goals in fields like medicine, agriculture, and environmental science. In conclusion, the concept of microbial communities as biological markets offers valuable insights into their intricate functioning and adaptability. It underscores the profound interplay between microbial ecosystems and human health and behavior, with far-reaching implications for multiple disciplines. To paraphrase Alfred Marshall, "the Mecca of the economist lies in economic microbiology."

Modeling the persistence of Opisthorchis viverrini worm burden after mass-drug administration and education campaigns with systematic adherence

Opisthorchis viverrini is a parasitic liver fluke contracted by consumption of raw fish, which affects over 10 million people in Southeast Asia despite sustained control efforts. Chronic infections are a risk factor for the often fatal bile duct cancer, cholangiocarcinoma. Previous modeling predicted rapid elimination of O. viverrini following yearly mass drug administration (MDA) campaigns. However, field data collected in affected populations shows persistence of infection, including heavy worm burden, after many years of repeated interventions. A plausible explanation for this observation is systematic adherence of individuals in health campaigns, such as MDA and education, with some individuals consistently missing treatment. We developed an agent-based model of O. viverrini which allows us to introduce various heterogeneities including systematic adherence to MDA and education campaigns at the individual level. We validate the agent-based model by comparing it to a previously published population-based model. We estimate the degree of systematic adherence to MDA and education campaigns indirectly, using epidemiological data collected in Lao PDR before and after 5 years of repeated MDA, education and sanitation improvement campaigns. We predict the impact of interventions deployed singly and in combination, with and without the estimated systematic adherence. We show how systematic adherence can substantially increase the time required to achieve reductions in worm burden. However, we predict that yearly MDA campaigns alone can result in a strong reduction of moderate and heavy worm burden, even under systematic adherence. We predict latrines and education campaigns to be particularly important for the reduction in overall prevalence, and therefore, ultimately, elimination. Our findings show how systematic adherence can explain the observed persistence of worm burden; while emphasizing the benefit of interventions for the entire population, even under systematic adherence. At the same time, the results highlight the substantial opportunity to further reduce worm burden if patterns of systematic adherence can be overcome.

Linking avian malaria parasitemia estimates from quantitative PCR and microscopy reveals new infection patterns in Hawai'i

Plasmodium parasites infect thousands of species and provide an exceptional system for studying host-pathogen dynamics, especially for multi-host pathogens. However, understanding these interactions requires an accurate assay of infection. Assessing Plasmodium infections using microscopy on blood smears often misses infections with low parasitemias (the fractions of cells infected), and biases in malaria prevalence estimates will differ among hosts that differ in mean parasitemias. We examined Plasmodium relictum infection and parasitemia using both microscopy of blood smears and quantitative polymerase chain reaction (qPCR) on 299 samples from multiple bird species in Hawai'i and fit models to predict parasitemias from qPCR cycle threshold (Ct) values. We used these models to quantify the extent to which microscopy underestimated infection prevalence and to more accurately estimate infection patterns for each species for a large historical study done by microscopy. We found that most qPCR-positive wild-caught birds in Hawaii had low parasitemias (Ct scores ≥35), which were rarely detected by microscopy. The fraction of infections missed by microscopy differed substantially among eight species due to differences in species' parasitemia levels. Infection prevalence was likely 4-5-fold higher than previous microscopy estimates for three introduced species, including Zosterops japonicus, Hawaii's most abundant forest bird, which had low average parasitemias. In contrast, prevalence was likely only 1.5-2.3-fold higher than previous estimates for Himatione sanguinea and Chlorodrepanis virens, two native species with high average parasitemias. Our results indicate that relative patterns of infection among species differ substantially from those observed in previous microscopy studies, and that differences depend on variation in parasitemias among species. Although microscopy of blood smears is useful for estimating the frequency of different Plasmodium stages and host attributes, more sensitive quantitative methods, including qPCR, are needed to accurately estimate and compare infection prevalence among host species.

Tuberculosis Infectiousness is Associated with Distinct Clinical and Inflammatory Profiles

Interrupting transmission events to prevent new acquisition of infection and disease is a critical part of tuberculosis (TB) control efforts. However, knowledge gaps in understanding the biology and determinants of TB transmission, including poor estimates of individual infectiousness and the lack of accurate and convenient biomarkers, undermine efforts to develop interventions. Cough-generated aerosol cultures have been found to predict TB transmission better than any microbiological or clinical markers in cohorts from Uganda and Brazil. We hypothesized that highly infectious individuals with pulmonary TB (defined as positive for cough aerosol cultures) have elevated inflammatory markers and unique transcriptional profiles compared to less infectious individuals (negative for cough aerosol cultures). We performed a prospective, longitudinal study using a cough aerosol sampling system as in other studies. We enrolled 142 participants with treatment-naïve pulmonary TB in Nairobi, Kenya, and assessed the association of clinical, microbiologic, and immunologic characteristics with Mtb aerosolization and transmission in 143 household members. Contacts of the forty-three aerosol culture-positive participants (30%) were more likely to have a positive IGRA (85% vs 53%, P = 0.005) and a higher median IGRA IFNγ level (P < 0.001, median 4.25 IU/ml (0.90-5.91) vs. 0.71 (0.01-3.56)) compared to aerosol culture-negative individuals. We found that higher bacillary burden, younger age, and larger mean upper arm circumference were associated with positive aerosol cultures. In addition, novel host inflammatory profiles, including elevated serum C-reactive protein and sputum cytokines, were associated with aerosol culture status. Notably, we found pre-treatment whole blood transcriptional profiles associated with aerosol culture status, independent of bacillary load. Together, these findings suggest that TB infectiousness is associated with epidemiologic characteristics and inflammatory signatures and that these features may be used to identify highly infectious persons. These results provide new public health tools and insights into TB pathogenesis.

Morbidity and mortality burden of COVID-19 in rural Madagascar: results from a longitudinal cohort and nested seroprevalence study

INTRODUCTION

Three years into the pandemic, there remains significant uncertainty about the true infection and mortality burden of COVID-19 in the World Health Organization Africa region. High quality, population-representative studies in Africa are rare and tend to be conducted in national capitals or large cities, leaving a substantial gap in our understanding of the impact of COVID-19 in rural, low-resource settings. Here, we estimated the spatio-temporal morbidity and mortality burden associated with COVID-19 in a rural health district of Madagascar until the first half of 2021.

METHODS

We integrated a nested seroprevalence study within a pre-existing longitudinal cohort conducted in a representative sample of 1600 households in Ifanadiana District, Madagascar. Socio-demographic and health information was collected in combination with dried blood spots for about 6500 individuals of all ages, which were analysed to detect IgG and IgM antibodies against four specific proteins of SARS-CoV-2 in a bead-based multiplex immunoassay. We evaluated spatio-temporal patterns in COVID-19 infection history and its associations with several geographic, socio-economic and demographic factors via logistic regressions.

RESULTS

Eighteen percent of people had been infected by April-June 2021, with seroprevalence increasing with individuals' age. COVID-19 primarily spread along the only paved road and in major towns during the first epidemic wave, subsequently spreading along secondary roads during the second wave to more remote areas. Wealthier individuals and those with occupations such as commerce and formal employment were at higher risk of being infected in the first wave. Adult mortality increased in 2020, particularly for older men for whom it nearly doubled up to nearly 40 deaths per 1000. Less than 10% of mortality in this period would be directly attributed to COVID-19 deaths if known infection fatality ratios are applied to observed seroprevalence in the district.

CONCLUSION

Our study provides a very granular understanding on COVID-19 transmission and mortality in a rural population of sub-Saharan Africa and suggests that the disease burden in these areas may have been substantially underestimated.

Quantifying Rift Valley fever virus transmission efficiency in a lamb-mosquito-lamb model

Rift Valley fever virus (RVFV) is a (re)emerging mosquito-borne pathogen impacting human and animal health. How RVFV spreads through a population depends on population-level and individual-level interactions between vector, host and pathogen. Here, we estimated the probability for RVFV to transmit to naive animals by experimentally exposing lambs to a bite of an infectious mosquito, and assessed if and how RVFV infection subsequently developed in the exposed animal. Aedes aegypti mosquitoes, previously infected via feeding on a viremic lamb, were used to expose naive lambs to the virus. Aedes aegypti colony mosquitoes were used as they are easy to maintain and readily feed in captivity. Other mosquito spp. could be examined with similar methodology. Lambs were exposed to either 1-3 (low exposure) or 7-9 (high exposure) infectious mosquitoes. All lambs in the high exposure group became viremic and showed characteristic signs of Rift Valley fever within 2-4 days post exposure. In contrast, 3 out of 12 lambs in the low exposure group developed viremia and disease, with similar peak-levels of viremia as the high exposure group but with some heterogeneity in the onset of viremia. These results suggest that the likelihood for successful infection of a ruminant host is affected by the number of infectious mosquitoes biting, but also highlights that a single bite of an infectious mosquito can result in disease. The per bite mosquito-to-host transmission efficiency was estimated at 28% (95% confidence interval: 15 - 47%). We subsequently combined this transmission efficiency with estimates for life traits of Aedes aegypti or related mosquitoes into a Ross-McDonald mathematical model to illustrate scenarios under which major RVFV outbreaks could occur in naïve populations (i.e., R0 >1). The model revealed that relatively high vector-to-host ratios as well as mosquitoes feeding preferably on competent hosts are required for R0 to exceed 1. Altogether, this study highlights the importance of experiments that mimic natural exposure to RVFV. The experiments facilitate a better understanding of the natural progression of disease and a direct way to obtain epidemiological parameters for mathematical models.

Model-based estimates of chikungunya epidemiological parameters and outbreak risk from varied data types

Assessing the factors responsible for differences in outbreak severity for the same pathogen is a challenging task, since outbreak data are often incomplete and may vary in type across outbreaks (e.g., daily case counts, serology, cases per household). We propose that outbreaks described with varied data types can be directly compared by using those data to estimate a common set of epidemiological parameters. To demonstrate this for chikungunya virus (CHIKV), we developed a realistic model of CHIKV transmission, along with a Bayesian inference method that accommodates any type of outbreak data that can be simulated. The inference method makes use of the fact that all data types arise from the same transmission process, which is simulated by the model. We applied these tools to data from three real-world outbreaks of CHIKV in Italy, Cambodia, and Bangladesh to estimate nine model parameters. We found that these populations differed in several parameters, including pre-existing immunity and house-to-house differences in mosquito activity. These differences resulted in posterior predictions of local CHIKV transmission risk that varied nearly fourfold: 16% in Italy, 28% in Cambodia, and 62% in Bangladesh. Our inference method and model can be applied to improve understanding of the epidemiology of CHIKV and other pathogens for which outbreaks are described with varied data types.

Sociodemographic Variables Can Guide Prioritized Testing Strategies for Epidemic Control in Resource-Limited Contexts

BACKGROUND

Targeted surveillance allows public health authorities to implement testing and isolation strategies when diagnostic resources are limited, and can be implemented via the consideration of social network topologies. However, it remains unclear how to implement such surveillance and control when network data are unavailable.

METHODS

We evaluated the ability of sociodemographic proxies of degree centrality to guide prioritized testing of infected individuals compared to known degree centrality. Proxies were estimated via readily available sociodemographic variables (age, gender, marital status, educational attainment, household size). We simulated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemics via a susceptible-exposed-infected-recovered individual-based model on 2 contact networks from rural Madagascar to test applicability of these findings to low-resource contexts.

RESULTS

Targeted testing using sociodemographic proxies performed similarly to targeted testing using known degree centralities. At low testing capacity, using proxies reduced infection burden by 22%-33% while using 20% fewer tests, compared to random testing. By comparison, using known degree centrality reduced the infection burden by 31%-44% while using 26%-29% fewer tests.

CONCLUSIONS

We demonstrate that incorporating social network information into epidemic control strategies is an effective countermeasure to low testing capacity and can be implemented via sociodemographic proxies when social network data are unavailable.

Variants of IL6, IL10, FCN2, RNASE3, IL12B and IL17B loci are associated with Schistosoma mansoni worm burden in the Albert Nile region of Uganda

BACKGROUND

Individuals genetically susceptible to high schistosomiasis worm burden may contribute disproportionately to transmission and could be prioritized for control. Identifying genes involved may guide development of therapy.

METHODOLOGY/PRINCIPAL FINDINGS

A cohort of 606 children aged 10-15 years were recruited in the Albert Nile region of Uganda and assessed for Schistosoma mansoni worm burden using the Up-Converting Particle Lateral Flow (UCP-LF) test detecting circulating anodic antigen (CAA), point-of-care Circulating Cathodic Antigen (POC-CCA) and Kato-Katz tests. Whole genome genotyping was conducted on 326 children comprising the top and bottom 25% of worm burden. Linear models were fitted to identify variants associated with worm burden in preselected candidate genes. Expression quantitative trait locus (eQTL) analysis was conducted for candidate genes with UCP-LF worm burden included as a covariate. Single Nucleotide Polymorphism loci associated with UCP-LF CAA included IL6 rs2066992 (OR = 0.43, p = 0.0006) and rs7793163 (OR = 2.0, p = 0.0007); IL21 SNP kgp513476 (OR 1.79, p = 0.0025) and IL17B SNP kgp708159 (OR = 0.35, p = 0.0028). A haplotype in the IL10 locus was associated with lower worm burden (OR = 0.53, p = 0.015) and overlapped SNPs rs1800896, rs1800871 and rs1800872. Significant haplotypes (p<0.05, overlapping significant SNP) associated with worm burden were observed in IL6 and the Th17 pathway IL12B and IL17B genes. There were significant eQTL in the IL6, IL5, IL21, IL25 and IFNG regions.

CONCLUSIONS

Variants associated with S. mansoni worm burden were in IL6, FCN2, RNASE3, IL10, IL12B and IL17B gene loci. However only eQTL associations remained significant after Bonferroni correction. In summary, immune balance, pathogen recognition and Th17 pathways may play a role in modulating Schistosoma worm burden. Individuals carrying risk variants may be targeted first in allocation of control efforts to reduce the burden of schistosomiasis in the community.

Food availability leads to more connected contact networks among peridomestic zoonotic reservoir hosts

The North American deermouse (Peromyscus maniculatus) is a reservoir host for many zoonotic pathogens. Deermice have been well studied, but few studies have attempted to understand social interactions within the species despite these interactions being key to understanding disease transmission. We performed an experiment to determine if supplemental food or nesting material affected social interactions of deermice and tested if interactions increased with increasing population density. We constructed three simulated buildings that received one of three treatments: food, nesting material, or control. Mice were tagged with passive integrated transponder (PIT) tags, and their movement in and out of buildings was monitored with PIT tag readers. PIT tag readings were used to create contact networks, assuming a contact if two deermice were in the same building at the same time. We found that buildings with food led to contact networks that were approximately 10 times more connected than buildings with nesting material or control buildings. We also saw a significant effect of population density on the average number of contacts per individual. These results suggest that food supplementation which is common in peridomestic settings, can significantly increase contacts between reservoir hosts, potentially leading to increased transmission of zoonotic viruses within the reservoir host and from reservoir hosts to humans.

Super-spreaders: a historical review

The term super-spreader is used for multiple, and sometimes even conflicting, purposes. The reasons for this can be traced back to its complex history. Forerunners of the super-spreader concept-in discussions of so-called dangerous carriers and in analyses of explosive outbreaks during the early 20th century-revolved primarily around gastrointestinal diseases, not respiratory ones. In 1957-58, the H2N2 influenza pandemic and Wells and Riley's studies on tuberculosis drew attention to both the viability of airborne transmission and the existence of significant heterogeneity in infectivity. The term super-spreader was coined in 1972, in relation to computer simulations of influenza epidemics. Initially, super-spreaders were simply an additional feature within stochastic models of epidemics, with little effect on an epidemic's eventual course. The term was later appropriated to explain why outbreaks of airborne diseases continued in vaccinated populations, defying the predictions of contact-transmission-based models. The content and meaning of the term continued to fluctuate, from the mathematical characterisation of sexually active people with gonorrhoea, through HIV carriers with highly infectious semen, to central nodes within a network. This Historical Review reconstructs the historical growth of the concept of super-spreading, and offers insight into its current, highly diversified use.

Direct mosquito feedings on dengue-2 virus-infected people reveal dynamics of human infectiousness

Dengue virus (DENV) transmission from humans to mosquitoes is a poorly documented, but critical component of DENV epidemiology. Magnitude of viremia is the primary determinant of successful human-to-mosquito DENV transmission. People with the same level of viremia, however, can vary in their infectiousness to mosquitoes as a function of other factors that remain to be elucidated. Here, we report on a field-based study in the city of Iquitos, Peru, where we conducted direct mosquito feedings on people naturally infected with DENV and that experienced mild illness. We also enrolled people naturally infected with Zika virus (ZIKV) after the introduction of ZIKV in Iquitos during the study period. Of the 54 study participants involved in direct mosquito feedings, 43 were infected with DENV-2, two with DENV-3, and nine with ZIKV. Our analysis excluded participants whose viremia was detectable at enrollment but undetectable at the time of mosquito feeding, which was the case for all participants with DENV-3 and ZIKV infections. We analyzed the probability of onward transmission during 50 feeding events involving 27 participants infected with DENV-2 based on the presence of infectious virus in mosquito saliva 7-16 days post blood meal. Transmission probability was positively associated with the level of viremia and duration of extrinsic incubation in the mosquito. In addition, transmission probability was influenced by the day of illness in a non-monotonic fashion; i.e., transmission probability increased until 2 days after symptom onset and decreased thereafter. We conclude that mildly ill DENV-infected humans with similar levels of viremia during the first two days after symptom onset will be most infectious to mosquitoes on the second day of their illness. Quantifying variation within and between people in their contribution to DENV transmission is essential to better understand the biological determinants of human infectiousness, parametrize epidemiological models, and improve disease surveillance and prevention strategies.

An invasive human commensal and a native marsupial maintain tick populations at the urban fringe

Ticks (Acari: Ixodidae) are major disease vectors globally making it increasingly important to understand how altered vertebrate communities in urban areas shape tick population dynamics. In urban landscapes of Australia, little is known about which native and introduced small mammals maintain tick populations preventing host-targeted tick management and leading to human-wildlife conflict. Here, we determined (1) larval, nymphal, and adult tick burdens on host species and potential drivers, (2) the number of ticks supported by the different host populations, and (3) the proportion of medically significant tick species feeding on the different host species in Northern Sydney. We counted 3551 ticks on 241 mammals at 15 sites and found that long-nosed bandicoots (Perameles nasuta) hosted more ticks of all life stages than other small mammals but introduced black rats (Rattus rattus) were more abundant at most sites (33%-100%) and therefore important in supporting larval and nymphal ticks in our study areas. Black rats and bandicoots hosted a greater proportion of medically significant tick species including Ixodes holocyclus than other hosts. Our results show that an introduced human commensal contributes to maintaining urban tick populations and suggests ticks could be managed by controlling rat populations on urban fringes.

Complexity in the dengue spreading: A network analysis approach

In an increasingly interconnected society, preventing epidemics has become a major challenge. Numerous infectious diseases spread between individuals by a vector, creating bipartite networks of infection with the characteristics of complex networks. In the case of dengue, a mosquito-borne disease, these infection networks include a vector-the Aedes aegypti mosquito-which has expanded its endemic area due to climate change. In this scenario, innovative approaches are essential to help public agents in the fight against the disease. Using an agent-based model, we investigated the network morphology of a dengue endemic region considering four different serotypes and a small population. The degree, betweenness, and closeness distributions are evaluated for the bipartite networks, considering the interactions up to the second order for each serotype. We observed scale-free features and heavy tails in the degree distribution and betweenness and quantified the decay of the degree distribution with a q-Gaussian fit function. The simulation results indicate that the spread of dengue is primarily driven by human-to-human and human-to-mosquito interaction, reinforcing the importance of controlling the vector to prevent episodes of epidemic outbreaks.

The Pampa del Indio project: sustainable vector control and long-term declines in the prevalence and abundance of Triatoma infestans infected with Trypanosoma cruzi in the Argentine Chaco

BACKGROUND

The Gran Chaco region is a major hotspot of Chagas disease. We implemented a 9-year program aimed at suppressing house infestation with Triatoma infestans and stopping vector-borne transmission to creole and indigenous (Qom) residents across Pampa del Indio municipality (Argentine Chaco). The aim of the present study was to assess the intervention effects on parasite-based transmission indices and the spatial distribution of the parasite, and test whether house-level variations in triatomine infection with Trypanosoma cruzi declined postintervention and were influenced by household ethnicity, persistent infestation linked to pyrethroid resistance and other determinants of bug infection.

METHODS

This longitudinal study assessed house infestation and bug infection with T. cruzi before and after spraying houses with pyrethroids and implemented systematic surveillance-and-response measures across four operational areas over the period 2007-2016. Live triatomines were individually examined for infection by optical microscopy or kinetoplast DNA (kDNA)-PCR and declared to be infected with T. cruzi when assessed positive by either method.

RESULTS

The prevalence of infection with T. cruzi was 19.4% among 6397 T. infestans examined. Infection ranged widely among the study areas (12.5-26.0%), household ethnicity (15.3-26.9%), bug ecotopes (1.8-27.2%) and developmental stages (5.9-27.6%), and decreased from 24.1% (baseline) to 0.9% (endpoint). Using random-intercept multiple logistic regression, the relative odds of bug infection strongly decreased as the intervention period progressed, and increased with baseline domestic infestation and bug stage and in Qom households. The abundance of infected bugs and the proportion of houses with ≥ 1 infected bug remained depressed postintervention and were more informative of area-wide risk status than the prevalence of bug infection. Global spatial analysis revealed sharp changes in the aggregation of bug infection after the attack phase. Baseline domestic infestation and baseline bug infection strongly predicted the future occurrence of bug infection, as did persistent domestic infestation in the area with multiple pyrethroid-resistant foci. Only 19% of houses had a baseline domestic infestation and 56% had ever had ≥ 1 infected bug.

CONCLUSIONS

Persistent bug infection postintervention was closely associated with persistent foci generated by pyrethroid resistance. Postintervention parasite-based indices closely agreed with human serosurveys at the study endpoint, suggesting transmission blockage. The program identified households and population subgroups for targeted interventions and opened new opportunities for risk prioritization and sustainable vector control and disease prevention.

Epidemiological connectivity between humans and animals across an urban landscape

Urbanization is predicted to be a key driver of disease emergence through human exposure to novel, animal-borne pathogens. However, while we suspect that urban landscapes are primed to expose people to novel animal-borne diseases, evidence for the mechanisms by which this occurs is lacking. To address this, we studied how bacterial genes are shared between wild animals, livestock, and humans (n = 1,428) across Nairobi, Kenya-one of the world's most rapidly developing cities. Applying a multilayer network framework, we show that low biodiversity (of both natural habitat and vertebrate wildlife communities), coupled with livestock management practices and more densely populated urban environments, promotes sharing of Escherichia coli-borne bacterial mobile genetic elements between animals and humans. These results provide empirical support for hypotheses linking resource provision, the biological simplification of urban landscapes, and human and livestock demography to urban dynamics of cross-species pathogen transmission at a landscape scale. Urban areas where high densities of people and livestock live in close association with synanthropes (species such as rodents that are more competent reservoirs for zoonotic pathogens) should be prioritized for disease surveillance and control.

Intrinsic randomness in epidemic modelling beyond statistical uncertainty

Uncertainty can be classified as either aleatoric (intrinsic randomness) or epistemic (imperfect knowledge of parameters). The majority of frameworks assessing infectious disease risk consider only epistemic uncertainty. We only ever observe a single epidemic, and therefore cannot empirically determine aleatoric uncertainty. Here, we characterise both epistemic and aleatoric uncertainty using a time-varying general branching process. Our framework explicitly decomposes aleatoric variance into mechanistic components, quantifying the contribution to uncertainty produced by each factor in the epidemic process, and how these contributions vary over time. The aleatoric variance of an outbreak is itself a renewal equation where past variance affects future variance. We find that, superspreading is not necessary for substantial uncertainty, and profound variation in outbreak size can occur even without overdispersion in the offspring distribution (i.e. the distribution of the number of secondary infections an infected person produces). Aleatoric forecasting uncertainty grows dynamically and rapidly, and so forecasting using only epistemic uncertainty is a significant underestimate. Therefore, failure to account for aleatoric uncertainty will ensure that policymakers are misled about the substantially higher true extent of potential risk. We demonstrate our method, and the extent to which potential risk is underestimated, using two historical examples.

Risk perception of Ebola virus disease and COVID-19 among transport drivers living in Ugandan border districts

Background

Cross-border movements between districts bordering Uganda and the Democratic Republic of Congo (DRC) are common due to the interdependence between populations on either side, though this increases the risk of the international spread of infectious diseases. Due to the nature of their work, boda boda drivers (motorcycle taxis), taxis and truck drivers continue to cross the border during epidemics. However, perceived risk of contracting and spreading communicable diseases may be influenced by several factors such as the level of education, packaging and perception of health care messages, limited interaction with local socio-cultural dynamics or personal experiences. This study aims to explore differences in movement patterns and risk perceptions as factors for transmission among transport drivers in Ugandan border districts during the 2018-2020 Ebola Virus Disease (EVD) epidemic and the current COVID-19 pandemic.

Methods

Between May and June 2021, in-depth interviews and focus group discussions were conducted with transport drivers in three Ugandan districts bordering DRC (Kasese, Kisoro and Hoima). Participants were asked about their knowledge and beliefs about EVD and COVID-19, perceived risk during epidemics, reasons for, and travel patterns during the EVD epidemic and COVID- 19 pandemic. A thematic content analysis was applied.

Results

Participants' awareness of EVD was higher than that of COVID-19 however, the risk of transmission of Ebola virus was perceived as a remote threat. Measures restricting mobility during the COVID-19 pandemic had a greater impact on transport drivers compared to those implemented during the EVD epidemic, and were perceived as prohibitive rather than protective, largely due to fear of reprisals by security officers. Despite this, drivers were unlikely to be able to comply with the restrictions as they relied on their work as a source of income.

Conclusion

The vulnerabilities of transport drivers should be considered in the context of epidemics such EVD and COVID-19 in Uganda. Policy makers should address these particularities and assess the impact of public health measures on transport drivers' mobility and involve them in designing of mobility-relatedpolicies.

Network analysis of pig movement data as an epidemiological tool: an Austrian case study

Animal movements represent a major risk for the spread of infectious diseases in the domestic swine population. In this study, we adopted methods from social network analysis to explore pig trades in Austria. We used a dataset of daily records of swine movements covering the period 2015-2021. We analyzed the topology of the network and its structural changes over time, including seasonal and long-term variations in the pig production activities. Finally, we studied the temporal dynamics of the network community structure. Our findings show that the Austrian pig production was dominated by small-sized farms while spatial farm density was heterogeneous. The network exhibited a scale-free topology but was very sparse, suggesting a moderate impact of infectious disease outbreaks. However, two regions (Upper Austria and Styria) may present a higher structural vulnerability. The network also showed very high assortativity between holdings from the same federal state. Dynamic community detection revealed a stable behavior of the clusters. Yet trade communities did not correspond to sub-national administrative divisions and may be an alternative zoning approach to managing infectious diseases. Knowledge about the topology, contact patterns, and temporal dynamics of the pig trade network can support optimized risk-based disease control and surveillance strategies.

Associations of water contact frequency, duration, and activities with schistosome infection risk: A systematic review and meta-analysis

BACKGROUND

Schistosomiasis is a water-borne parasitic disease which affects over 230 million people globally. The relationship between contact with open freshwater bodies and the likelihood of schistosome infection remains poorly quantified despite its importance for understanding transmission and parametrising transmission models.

METHODS

We conducted a systematic review to estimate the average effect of water contact duration, frequency, and activities on schistosome infection likelihood. We searched Embase, MEDLINE (including PubMed), Global Health, Global Index Medicus, Web of Science, and the Cochrane Central Register of Controlled Trials from inception until May 13, 2022. Observational and interventional studies reporting odds ratios (OR), hazard ratios (HR), or sufficient information to reconstruct effect sizes on individual-level associations between water contact and infection with any Schistosoma species were eligible for inclusion. Random-effects meta-analysis with inverse variance weighting was used to calculate pooled ORs and 95% confidence intervals (CIs).

RESULTS

We screened 1,411 studies and included 101 studies which represented 192,691 participants across Africa, Asia, and South America. Included studies mostly reported on water contact activities (69%; 70/101) and having any water contact (33%; 33/101). Ninety-six percent of studies (97/101) used surveys to measure exposure. A meta-analysis of 33 studies showed that individuals with water contact were 3.14 times more likely to be infected (OR 3.14; 95% CI: 2.08-4.75) when compared to individuals with no water contact. Subgroup analyses showed that the positive association of water contact with infection was significantly weaker in children compared to studies which included adults and children (OR 1.67; 95% CI: 1.04-2.69 vs. OR 4.24; 95% CI: 2.59-6.97). An association of water contact with infection was only found in communities with ≥10% schistosome prevalence. Overall heterogeneity was substantial (I2 = 93%) and remained high across all subgroups, except in direct observation studies (I2 range = 44%-98%). We did not find that occupational water contact such as fishing and agriculture (OR 2.57; 95% CI: 1.89-3.51) conferred a significantly higher risk of schistosome infection compared to recreational water contact (OR 2.13; 95% CI: 1.75-2.60) or domestic water contact (OR 1.91; 95% CI: 1.47-2.48). Higher duration or frequency of water contact did not significantly modify infection likelihood. Study quality across analyses was largely moderate or poor.

CONCLUSIONS

Any current water contact was robustly associated with schistosome infection status, and this relationship held across adults and children, and schistosomiasis-endemic areas with prevalence greater than 10%. Substantial gaps remain in published studies for understanding interactions of water contact with age and gender, and the influence of these interactions for infection likelihood. As such, more empirical studies are needed to accurately parametrise exposure in transmission models. Our results imply the need for population-wide treatment and prevention strategies in endemic settings as exposure within these communities was not confined to currently prioritised high-risk groups such as fishing populations.

Superspreading, overdispersion and their implications in the SARS-CoV-2 (COVID-19) pandemic: a systematic review and meta-analysis of the literature

BACKGROUND

A recurrent feature of infectious diseases is the observation that different individuals show different levels of secondary transmission. This inter-individual variation in transmission potential is often quantified by the dispersion parameter k. Low values of k indicate a high degree of variability and a greater probability of superspreading events. Understanding k for COVID-19 across contexts can assist policy makers prepare for future pandemics.

METHODS

A literature search following a systematic approach was carried out in PubMed, Embase, Web of Science, Cochrane Library, medRxiv, bioRxiv and arXiv to identify publications containing epidemiological findings on superspreading in COVID-19. Study characteristics, epidemiological data, including estimates for k and R0, and public health recommendations were extracted from relevant records.

RESULTS

The literature search yielded 28 peer-reviewed studies. The mean k estimates ranged from 0.04 to 2.97. Among the 28 studies, 93% reported mean k estimates lower than one, which is considered as marked heterogeneity in inter-individual transmission potential. Recommended control measures were specifically aimed at preventing superspreading events. The combination of forward and backward contact tracing, timely confirmation of cases, rapid case isolation, vaccination and preventive measures were suggested as important components to suppress superspreading.

CONCLUSIONS

Superspreading events were a major feature in the pandemic of SARS-CoV-2. On the one hand, this made outbreaks potentially more explosive but on the other hand also more responsive to public health interventions. Going forward, understanding k is critical for tailoring public health measures to high-risk groups and settings where superspreading events occur.

Epidemiological inference for emerging viruses using segregating sites

Epidemiological models are commonly fit to case and pathogen sequence data to estimate parameters and to infer unobserved disease dynamics. Here, we present an inference approach based on sequence data that is well suited for model fitting early on during the expansion of a viral lineage. Our approach relies on a trajectory of segregating sites to infer epidemiological parameters within a Sequential Monte Carlo framework. Using simulated data, we first show that our approach accurately recovers key epidemiological quantities under a single-introduction scenario. We then apply our approach to SARS-CoV-2 sequence data from France, estimating a basic reproduction number of approximately 2.3-2.7 under an epidemiological model that allows for multiple introductions. Our approach presented here indicates that inference approaches that rely on simple population genetic summary statistics can be informative of epidemiological parameters and can be used for reconstructing infectious disease dynamics during the early expansion of a viral lineage.

Physical distancing versus testing with self-isolation for controlling an emerging epidemic

Two distinct strategies for controlling an emerging epidemic are physical distancing and regular testing with self-isolation. These strategies are especially important before effective vaccines or treatments become widely available. The testing strategy has been promoted frequently but used less often than physical distancing to mitigate COVID-19. We compared the performance of these strategies in an integrated epidemiological and economic model that includes a simple representation of transmission by "superspreading," wherein a relatively small fraction of infected individuals cause a large share of infections. We examined the economic benefits of distancing and testing over a wide range of conditions, including variations in the transmissibility and lethality of the disease meant to encompass the most prominent variants of COVID-19 encountered so far. In a head-to-head comparison using our primary parameter values, both with and without superspreading and a declining marginal value of mortality risk reductions, an optimized testing strategy outperformed an optimized distancing strategy. In a Monte Carlo uncertainty analysis, an optimized policy that combined the two strategies performed better than either one alone in more than 25% of random parameter draws. Insofar as diagnostic tests are sensitive to viral loads, and individuals with high viral loads are more likely to contribute to superspreading events, superspreading enhances the relative performance of testing over distancing in our model. Both strategies performed best at moderate levels of transmissibility, somewhat lower than the transmissibility of the ancestral strain of SARS-CoV-2.

Positive feedback loops exacerbate the influence of superspreaders in disease transmission

Superspreaders are recognized as being important drivers of disease spread. However, models to date have assumed random occurrence of superspreaders, irrespective of whom they were infected by. Evidence suggests though that those individuals infected by superspreaders may be more likely to become superspreaders themselves. Here, we begin to explore, theoretically, the effects of such a positive feedback loop on (1) the final epidemic size, (2) the herd immunity threshold, (3) the basic reproduction number, R₀, and (4) the peak prevalence of superspreaders, using a generic model for a hypothetical acute viral infection and illustrative parameter values. We show that positive feedback loops can have a profound effect on our chosen epidemic outcomes, even when the transmission advantage of superspreaders is moderate, and despite peak prevalence of superspreaders remaining low. We argue that positive superspreader feedback loops in different infectious diseases, including SARS-CoV-2, should be investigated further, both theoretically and empirically.

Transmission dynamics of lyssavirus in Myotis myotis: mechanistic modelling study based on longitudinal seroprevalence data

We investigated the transmission dynamics of lyssavirus in Myotis myotis and Myotis blythii, using serological, virological, demographic and ecological data collected between 2015 and 2022 from two maternity colonies in northern Italian churches. Despite no lyssavirus detection in 556 bats sampled over 11 events by reverse transcription-polymerase chain reaction (RT-PCR), 36.3% of 837 bats sampled over 27 events showed neutralizing antibodies to European bat lyssavirus 1, with a significant increase in summers. By fitting sets of mechanistic models to seroprevalence data, we investigated factors that influenced lyssavirus transmission within and between years. Five models were selected as a group of final models: in one model, a proportion of exposed bats (median model estimate: 5.8%) became infectious and died while the other exposed bats recovered with immunity without becoming infectious; in the other four models, all exposed bats became infectious and recovered with immunity. The final models supported that the two colonies experienced seasonal outbreaks driven by: (i) immunity loss particularly during hibernation, (ii) density-dependent transmission, and (iii) a high transmission rate after synchronous birthing. These findings highlight the importance of understanding ecological factors, including colony size and synchronous birthing timing, and potential infection heterogeneities to enable more robust assessments of lyssavirus spillover risk.

Modeling the contribution of antibody attack rates to single and dual helminth infections in a natural system

Within-host models of infection can provide important insights into the processes that affect parasite spread and persistence in host populations. However, modeling can be limited by the availability of empirical data, a problem commonly encountered in natural systems. Here, we used six years of immune-infection observations of two gastrointestinal helminths (Trichostrongylus retortaeformis and Graphidium strigosum) from a population of European rabbits (Oryctolagus cuniculus) to develop an age-dependent, mathematical model that explicitly included species-specific and cross-reacting antibody (IgA and IgG) responses to each helminth in hosts with single or dual infections. Different models of single infection were formally compared to test alternative mechanisms of parasite regulation. The two models that best described single infections of each helminth species were then coupled through antibody cross-immunity to examine how the presence of one species could alter the host immune response to, and the within-host dynamics of, the other species. For both single infections, model selection suggested that either IgA or IgG responses could equally explain the observed parasite intensities by host age. However, the antibody attack rate and affinity level changed between the two helminths, it was stronger against T. retortaeformis than against G. strigosum and caused contrasting age-intensity profiles. When the two helminths coinfect the same host, we found variation of the species-specific antibody response to both species together with an asymmetric cross-immune response driven by IgG. Lower attack rate and affinity of antibodies in dual than single infections contributed to the significant increase of both helminth intensities. By combining mathematical modeling with immuno-infection data, our work provides a tractable model framework for disentangling some of the complexities generated by host-parasite and parasite-parasite interactions in natural systems.

Antimicrobial Resistance in the Global Health Network: Known Unknowns and Challenges for Efficient Responses in the 21st Century

Antimicrobial resistance (AMR) is one of the Global Health challenges of the 21st century. The inclusion of AMR on the global map parallels the scientific, technological, and organizational progress of the healthcare system and the socioeconomic changes of the last 100 years. Available knowledge about AMR has mostly come from large healthcare institutions in high-income countries and is scattered in studies across various fields, focused on patient safety (infectious diseases), transmission pathways and pathogen reservoirs (molecular epidemiology), the extent of the problem at a population level (public health), their management and cost (health economics), cultural issues (community psychology), and events associated with historical periods (history of science). However, there is little dialogue between the aspects that facilitate the development, spread, and evolution of AMR and various stakeholders (patients, clinicians, public health professionals, scientists, economic sectors, and funding agencies). This study consists of four complementary sections. The first reviews the socioeconomic factors that have contributed to building the current Global Healthcare system, the scientific framework in which AMR has traditionally been approached in such a system, and the novel scientific and organizational challenges of approaching AMR in the fourth globalization scenario. The second discusses the need to reframe AMR in the current public health and global health contexts. Given that the implementation of policies and guidelines are greatly influenced by AMR information from surveillance systems, in the third section, we review the unit of analysis ("the what" and "the who") and the indicators (the "operational units of surveillance") used in AMR and discuss the factors that affect the validity, reliability, and comparability of the information to be applied in various healthcare (primary, secondary, and tertiary), demographic, and economic contexts (local, regional, global, and inter-sectorial levels). Finally, we discuss the disparities and similarities between distinct stakeholders' objectives and the gaps and challenges of combatting AMR at various levels. In summary, this is a comprehensive but not exhaustive revision of the known unknowns about how to analyze the heterogeneities of hosts, microbes, and hospital patches, the role of surrounding ecosystems, and the challenges they represent for surveillance, antimicrobial stewardship, and infection control programs, which are the traditional cornerstones for controlling AMR in human health.

A practical guide to mathematical methods for estimating infectious disease outbreak risks

Mathematical models are increasingly used throughout infectious disease outbreaks to guide control measures. In this review article, we focus on the initial stages of an outbreak, when a pathogen has just been observed in a new location (e.g., a town, region or country). We provide a beginner's guide to two methods for estimating the risk that introduced cases lead to sustained local transmission (i.e., the probability of a major outbreak), as opposed to the outbreak fading out with only a small number of cases. We discuss how these simple methods can be extended for epidemiological models with any level of complexity, facilitating their wider use, and describe how estimates of the probability of a major outbreak can be used to guide pathogen surveillance and control strategies. We also give an overview of previous applications of these approaches. This guide is intended to help quantitative researchers develop their own epidemiological models and use them to estimate the risks associated with pathogens arriving in new host populations. The development of these models is crucial for future outbreak preparedness. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Genotyping of Anopheles mosquito blood meals reveals nonrandom human host selection: implications for human-to-mosquito Plasmodium falciparum transmission

BACKGROUND

Control of malaria parasite transmission can be enhanced by understanding which human demographic groups serve as the infectious reservoirs. Because vector biting can be heterogeneous, some infected individuals may contribute more to human-to-mosquito transmission than others. Infection prevalence peaks in school-age children, but it is not known how often they are fed upon. Genotypic profiling of human blood permits identification of individual humans who were bitten. The present investigation used this method to estimate which human demographic groups were most responsible for transmitting malaria parasites to Anopheles mosquitoes. It was hypothesized that school-age children contribute more than other demographic groups to human-to-mosquito malaria transmission.

METHODS

In a region of moderate-to-high malaria incidence in southeastern Malawi, randomly selected households were surveyed to collect human demographic information and blood samples. Blood-fed, female Anopheles mosquitoes were sampled indoors from the same houses. Genomic DNA from human blood samples and mosquito blood meals of human origin was genotyped using 24 microsatellite loci. The resultant genotypes were matched to identify which individual humans were sources of blood meals. In addition, Plasmodium falciparum DNA in mosquito abdomens was detected with polymerase chain reaction. The combined results were used to identify which humans were most frequently bitten, and the P. falciparum infection prevalence in mosquitoes that resulted from these blood meals.

RESULTS

Anopheles females selected human hosts non-randomly and fed on more than one human in 9% of the blood meals. Few humans contributed most of the blood meals to the Anopheles vector population. Children ≤ 5 years old were under-represented in mosquito blood meals while older males (31-75 years old) were over-represented. However, the largest number of malaria-infected blood meals was from school age children (6-15 years old).

CONCLUSIONS

The results support the hypothesis that humans aged 6-15 years are the most important demographic group contributing to the transmission of P. falciparum to the Anopheles mosquito vectors. This conclusion suggests that malaria control and prevention programmes should enhance efforts targeting school-age children and males.

Intraspecific trait variation and changing life-history strategies explain host community disease risk along a temperature gradient

Predicting how climate change will affect disease risk is complicated by the fact that changing environmental conditions can affect disease through direct and indirect effects. Species with fast-paced life-history strategies often amplify disease, and changing climate can modify life-history composition of communities thereby altering disease risk. However, individuals within a species can also respond to changing conditions with intraspecific trait variation. To test the effect of temperature, as well as inter- and intraspecifc trait variation on community disease risk, we measured foliar disease and specific leaf area (SLA; a proxy for life-history strategy) on more than 2500 host (plant) individuals in 199 communities across a 1101 m elevational gradient in southeastern Switzerland. There was no direct effect of increasing temperature on disease. Instead, increasing temperature favoured species with higher SLA, fast-paced life-history strategies. This effect was balanced by intraspecific variation in SLA: on average, host individuals expressed lower SLA with increasing temperature, and this effect was stronger among species adapted to warmer temperatures and lower latitudes. These results demonstrate how impacts of changing temperature on disease may depend on how temperature combines and interacts with host community structure while indicating that evolutionary constraints can determine how these effects are manifested under global change. This article is part of the theme issue 'Infectious disease ecology and evolution in a changing world'.

Association between polymorphisms of IL4, IL13, IL10, STAT6 and IFNG genes, cytokines and immunoglobulin E levels with high burden of Schistosoma mansoni in children from schistosomiasis endemic areas of Cameroon

Eliminating schistosomiasis as a public health problem by 2030 requires a better understanding of the disease transmission, especially the asymmetric distribution of worm burden in individuals living and sharing the same environment. It is in this light that this study was designed to identify human genetic determinants associated with high burden of S. mansoni and also with the plasma concentrations of IgE and four cytokines in children from two schistosomiasis endemic areas of Cameroon. In school-aged children of schistosomiasis endemic areas of Makenene and Nom-Kandi of Cameroon, S. mansoni infections and their infection intensities were evaluated in urine and stool samples using respectively the Point-of-care Circulating Cathodic Antigen test (POC-CCA) and the Kato Katz (KK) test. Thereafter, blood samples were collected in children harbouring high burden of schistosome infections as well as in their parents and siblings. DNA extracts and plasma were obtained from blood. Polymorphisms at 14 loci of five genes were assessed using PCR-restriction fragment length polymorphism and amplification-refractory mutation system. The ELISA test enabled to determine the plasma concentrations of IgE, IL-13, IL-10, IL-4 and IFN-γ. The prevalence of S. mansoni infections was significantly higher (P < 0.0001 for POC-CCA; P = 0.001 for KK) in Makenene (48.6% for POC-CCA and 7.9% for KK) compared to Nom-Kandi (31% for POC-CCA and 4.3% for KK). The infection intensities were also higher (P < 0.0001 for POC-CCA; P = 0.001 for KK) in children from Makenene than those from Nom-Kandi. The allele C of SNP rs3024974 of STAT6 was associated with an increased risk of bearing high burden of S. mansoni both in the additive (p = 0.009) and recessive model (p = 0.01) while the allele C of SNP rs1800871 of IL10 was protective (p = 0.0009) against high burden of S. mansoni. The alleles A of SNP rs2069739 of IL13 and G of SNP rs2243283 of IL4 were associated with an increased risk of having low plasma concentrations of IL-13 (P = 0.04) and IL-10 (P = 0.04), respectively. This study showed that host genetic polymorphisms may influence the outcome (high or low worm burden) of S. mansoni infections and also the plasma concentrations of some cytokines.

Super-spreaders of novel coronaviruses that cause SARS, MERS and COVID-19: A systematic review

PURPOSE

Most index cases with novel coronavirus infections transmit disease to just one or two other individuals, but some individuals "super-spread"-they infect many secondary cases. Understanding common factors that super-spreaders may share could inform outbreak models, and be used to guide contact tracing during outbreaks.

METHODS

We searched in MEDLINE, Scopus, and preprints to identify studies about people documented as transmitting pathogens that cause SARS, MERS, or COVID-19 to at least nine other people. We extracted data to describe them by age, sex, location, occupation, activities, symptom severity, any underlying conditions, disease outcome and undertook quality assessment for outbreaks published by June 2021.

RESULTS

The most typical super-spreader was a male age 40+. Most SARS or MERS super-spreaders were very symptomatic, the super-spreading occurred in hospital settings and frequently the individual died. In contrast, COVID-19 super-spreaders often had very mild disease and most COVID-19 super-spreading happened in community settings.

CONCLUSIONS

SARS and MERS super-spreaders were often symptomatic, middle- or older-age adults who had a high mortality rate. In contrast, COVID-19 super-spreaders tended to have mild disease and were any adult age. More outbreak reports should be published with anonymized but useful demographic information to improve understanding of super-spreading, super-spreaders, and the settings in which super-spreading happens.

Inapparent infections shape the transmission heterogeneity of dengue

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

A statistical framework for tracking the time-varying superspreading potential of COVID-19 epidemic

Timely detection of an evolving event of an infectious disease with superspreading potential is imperative for territory-wide disease control as well as preventing future outbreaks. While the reproduction number (R) is a commonly-adopted metric for disease transmissibility, the transmission heterogeneity quantified by dispersion parameter k, a metric for superspreading potential is seldom tracked. In this study, we developed an estimation framework to track the time-varying risk of superspreading events (SSEs) and demonstrated the method using the three epidemic waves of COVID-19 in Hong Kong. Epidemiological contact tracing data of the confirmed COVID-19 cases from 23 January 2020 to 30 September 2021 were obtained. By applying branching process models, we jointly estimated the time-varying R and k. Individual-based outbreak simulations were conducted to compare the time-varying assessment of the superspreading potential with the typical non-time-varying estimate of k over a period of time. We found that the COVID-19 transmission in Hong Kong exhibited substantial superspreading during the initial phase of the epidemics, with only 1 % (95 % Credible interval [CrI]: 0.6-2 %), 5 % (95 % CrI: 3-7 %) and 10 % (95 % CrI: 8-14 %) of the most infectious cases generated 80 % of all transmission for the first, second and third epidemic waves, respectively. After implementing local public health interventions, R estimates dropped gradually and k estimates increased thereby reducing the risk of SSEs to approaching zero. Outbreak simulations indicated that the non-time-varying estimate of k may overlook the possibility of large outbreaks. Hence, an estimation of the time-varying k as a compliment of R as a monitoring of both disease transmissibility and superspreading potential, particularly when public health interventions were relaxed is crucial for minimizing the risk of future outbreaks.

Application of Eugenol in Poultry to Control Salmonella Colonization and Spread

The poultry sector is an essential component of agriculture that has experienced unprecedented growth during the last few decades. It is especially true for the United States, where the average intake of chicken meat increased from 10 pounds (4.5 kg) per person in 1940 to 65.2 pounds (29.6 kg) per person in 2018, while the country produced 113 billion eggs in 2019 alone. Besides providing nutrition and contributing significantly to the economy, chicken is also a natural reservoir of Salmonella, which is responsible for salmonellosis in humans, one of the significant foodborne illnesses around the globe. The increasing use of chicken manure and antibiotics increases the spread of Salmonella and selects for multi-drug resistant strains. Various plant extracts, primarily essential oils, have been investigated for their antimicrobial activities. The multiple ways through which these plant-derived compounds exert their antimicrobial effects make the development of resistance against them unlikely. Eugenol, an aromatic oil primarily found in clove and cinnamon, has shown antimicrobial activities against various pathogenic bacteria. A few reports have also highlighted the anti-Salmonella effects of eugenol in chicken, especially in reducing the colonization by Salmonella Enteritidis and Salmonella Typhimurium, the primary Salmonella species responsible for human salmonellosis. Besides limiting Salmonella infection in chicken, the supplementation of eugenol also significantly improves intestinal health, improving overall well-being. In this review, we highlight the rising incidences of salmonellosis worldwide and the factors increasing its prevalence. We then propose the usage of eugenol as a natural feed supplement for containing Salmonella in chicken.

Individual variation in Plasmodium vivax malaria risk: Are repeatedly infected people just unlucky?

Extensive research has examined why some people have frequent Plasmodium falciparum malaria episodes in sub-Saharan Africa while others remain free of disease most of the time. In contrast, malaria risk heterogeneity remains little studied in regions where P. vivax is the dominant species. Are repeatedly infected people in vivax malaria settings such as the Amazon just unlucky? Here, we briefly review evidence that human genetic polymorphism and acquired immunity after repeated exposure to parasites can modulate the risk of P. vivax infection and disease in predictable ways. One-fifth of the hosts account for 80% or more of the community-wide vivax malaria burden and contribute disproportionally to onward transmission, representing a priority target of more intensive interventions to achieve malaria elimination. Importantly, high-risk individuals eventually develop clinical immunity, even in areas with very low or residual malaria transmission, and may constitute a large but silent parasite reservoir.

Neuromodulation and differential learning across mosquito species

Mosquitoes can change their feeding behaviours based on past experiences, such as shifting from biting animals to biting humans or avoiding defensive hosts (Wolff & Riffell 2018 J. Exp. Biol. 221, jeb157131. (doi:10.1242/jeb.157131)). Dopamine is a critical neuromodulator for insects, allowing flexibility in their feeding preferences, but its role in the primary olfactory centre, the antennal lobe (AL), remains unclear (Vinauger et al. 2018 Curr. Biol. 28, 333-344.e8. (doi:10.1016/j.cub.2017.12.015)). It is also unknown whether mosquitoes can learn some odours and not others, or whether different species learn the same odour cues. We assayed aversive olfactory learning in four mosquito species with different host preferences, and found that they differentially learn odours salient to their preferred host. Mosquitoes that prefer humans learned odours found in mammalian skin, but not a flower odour, and a nectar-feeding species only learned a floral odour. Comparing the brains of these four species revealed significantly different innervation patterns in the AL by dopaminergic neurons. Calcium imaging in the Aedes aegypti AL and three-dimensional image analyses of dopaminergic innervation show that glomeruli tuned to learnable odours have significantly higher dopaminergic innervation. Changes in dopamine expression in the insect AL may be an evolutionary mechanism to adapt olfactory learning circuitry without changing brain structure and confer to mosquitoes an ability to adapt to new hosts.

Quantifying Mycobacterium tuberculosis Transmission Dynamics Across Global Settings: A Systematic Analysis

The degree to which individual heterogeneity in the production of secondary cases ("superspreading") affects tuberculosis (TB) transmission has not been systematically studied. We searched for population-based or surveillance studies in which whole genome sequencing was used to estimate TB transmission and in which the size distributions of putative TB transmission clusters were enumerated. We fitted cluster-size-distribution data to a negative binomial branching process model to jointly infer the transmission parameters $R$ (the reproduction number) and the dispersion parameter, $k$, which quantifies the propensity of superspreading in a population (generally, lower values of $k$ ($<1.0$) suggest increased heterogeneity). Of 4,796 citations identified in our initial search, 9 studies from 8 global settings met the inclusion criteria (n = 5 studies of all TB; n = 4 studies of drug-resistant TB). Estimated $R$ values (range, 0.10-0.73) were below 1.0, consistent with declining epidemics in the included settings; estimated $k$ values were well below 1.0 (range, 0.02-0.48), indicating the presence of substantial individual-level heterogeneity in transmission across all settings. We estimated that a minority of cases (range, 2%-31%) drive the majority (80%) of ongoing TB transmission at the population level. Identifying sources of heterogeneity and accounting for them in TB control may have a considerable impact on mitigating TB transmission.

Pathogen transmission modes determine contact network structure, altering other pathogen characteristics

Pathogen traits can vary greatly and heavily impact the ability of a pathogen to persist in a population. Although this variation is fundamental to disease ecology, little is known about the evolutionary pressures that drive these differences, particularly where they interact with host behaviour. We hypothesized that host behaviours relevant to different transmission routes give rise to differences in contact network structure, constraining the space over which pathogen traits can evolve to maximize fitness. Our analysis of 232 contact networks across mammals, birds, reptiles, amphibians, arthropods, fish and molluscs found that contact network topology varies by contact type, most notably in networks that are representative of fluid-exchange transmission. Using infectious disease model simulations, we showed that these differences in network structure suggest pathogens transmitted through fluid-exchange contact types will need traits associated with high transmissibility to successfully proliferate, compared to pathogens that transmit through other types of contact. These findings were supported through a review of known traits of pathogens that transmit in humans. Our work demonstrates that contact network structure may drive the evolution of compensatory pathogen traits according to transmission strategy, providing essential context for understanding pathogen evolution and ecology.

A Review of the Ring Trial Design for Evaluating Ring Interventions for Infectious Diseases

In trials of infectious disease interventions, rare outcomes and unpredictable spatiotemporal variation can introduce bias, reduce statistical power, and prevent conclusive inferences. Spillover effects can complicate inference if individual randomization is used to gain efficiency. Ring trials are a type of cluster-randomized trial that may increase efficiency and minimize bias, particularly in emergency and elimination settings with strong clustering of infection. They can be used to evaluate ring interventions, which are delivered to individuals in proximity to or contact with index cases. We conducted a systematic review of ring trials, compare them with other trial designs for evaluating ring interventions, and describe strengths and weaknesses of each design. Of 849 articles and 322 protocols screened, we identified 26 ring trials, 15 cluster-randomized trials, 5 trials that randomized households or individuals within rings, and 1 individually randomized trial. The most common interventions were postexposure prophylaxis (n = 23) and focal mass drug administration and screening and treatment (n = 7). Ring trials require robust surveillance systems and contact tracing for directly transmitted diseases. For rare diseases with strong spatiotemporal clustering, they may have higher efficiency and internal validity than cluster-randomized designs, in part because they ensure that no clusters are excluded from analysis due to zero cluster incidence. Though more research is needed to compare them with other types of trials, ring trials hold promise as a design that can increase trial speed and efficiency while reducing bias.

Relationships between biological age, distance from aquatic habitats and pyrethroid resistance status of Anopheles funestus mosquitoes in south-eastern Tanzania

BACKGROUND

Malaria transmission can be highly heterogeneous between and within localities, and is influenced by factors such as survival and biting frequencies of Anopheles mosquitoes. This study investigated the relationships between the biological age, distance from aquatic habitats and pyrethroid resistance status of Anopheles funestus mosquitoes, which currently dominate malaria transmission in south-east Tanzania. The study also examined how such relationships may influence malaria transmission and control.

METHODS

Female An. funestus were collected in houses located 50-100 m, 150-200 m or over 200 m from the nearest known aquatic habitats. The mosquitoes were exposed to 1×, 5× and 10× the diagnostic doses of deltamethrin or permethrin, or to the synergist, piperonyl butoxide (PBO) followed by the pyrethroids, then monitored for 24 h-mortality. Ovaries of exposed and non-exposed mosquitoes were dissected to assess parity as a proxy for biological age. Adults emerging from larval collections in the same villages were tested against the same insecticides at 3-5, 8-11 or 17-20 days old.

FINDINGS

Mosquitoes collected nearest to the aquatic habitats (50-100 m) had the lowest mortalities compared to other distances, with a maximum of 51% mortality at 10× permethrin. For the age-synchronized mosquitoes collected as larvae, the insecticide-induced mortality assessed at both the diagnostic and multiplicative doses (1×, 5× and 10×) increased with mosquito age. The highest mortalities at 1× doses were observed among the oldest mosquitoes (17-20 days). At 10× doses, mortalities were 99% (permethrin) and 76% (deltamethrin) among 8-11 day-olds compared to 80% (permethrin) and 58% (deltamethrin) among 3-5 day-olds. Pre-exposure to PBO increased the potency of both pyrethroids. The proportion of parous females was highest among mosquitoes collected farthest from the habitats.

CONCLUSION

In this specific setting, older An. funestus and those collected farthest from the aquatic habitats (near the centre of the village) were more susceptible to pyrethroids than the younger ones and those caught nearest to the habitats. These findings suggest that pyrethroid-based interventions may remain at least moderately effective despite widespread pyrethroid-resistance, by killing the older, less-resistant and potentially-infective mosquitoes. Further studies should investigate how and whether these observations could be exploited to optimize malaria control in different settings.

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

Background

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

Methods

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

Results

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

Conclusions

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

The use of routine health facility data for micro-stratification of malaria risk in mainland Tanzania

Background

Current efforts to estimate the spatially diverse malaria burden in malaria-endemic countries largely involve the use of epidemiological modelling methods for describing temporal and spatial heterogeneity using sparse interpolated prevalence data from periodic cross-sectional surveys. However, more malaria-endemic countries are beginning to consider local routine data for this purpose. Nevertheless, routine information from health facilities (HFs) remains widely under-utilized despite improved data quality, including increased access to diagnostic testing and the adoption of the electronic District Health Information System (DHIS2). This paper describes the process undertaken in mainland Tanzania using routine data to develop a high-resolution, micro-stratification risk map to guide future malaria control efforts.

Methods

Combinations of various routine malariometric indicators collected from 7098 HFs were assembled across 3065 wards of mainland Tanzania for the period 2017–2019. The reported council-level prevalence classification in school children aged 5–16 years (PfPR5–16) was used as a benchmark to define four malaria risk groups. These groups were subsequently used to derive cut-offs for the routine indicators by minimizing misclassifications and maximizing overall agreement. The derived-cutoffs were converted into numbered scores and summed across the three indicators to allocate wards into their overall risk stratum.

Results

Of 3065 wards, 353 were assigned to the very low strata (10.5% of the total ward population), 717 to the low strata (28.6% of the population), 525 to the moderate strata (16.2% of the population), and 1470 to the high strata (39.8% of the population). The resulting micro-stratification revealed malaria risk heterogeneity within 80 councils and identified wards that would benefit from community-level focal interventions, such as community-case management, indoor residual spraying and larviciding.

Conclusion

The micro-stratification approach employed is simple and pragmatic, with potential to be easily adopted by the malaria programme in Tanzania. It makes use of available routine data that are rich in spatial resolution and that can be readily accessed allowing for a stratification of malaria risk below the council level. Such a framework is optimal for supporting evidence-based, decentralized malaria control planning, thereby improving the effectiveness and allocation efficiency of malaria control interventions.

Long-term temporal trends in gastrointestinal parasite infection in wild Soay sheep

Monitoring the prevalence and abundance of parasites over time is important for addressing their potential impact on host life histories, immunological profiles and their influence as a selective force. Only long-term ecological studies have the potential to shed light on both the temporal trends in infection prevalence and abundance and the drivers of such trends, because of their ability to dissect drivers that may be confounded over shorter time scales. Despite this, only a relatively small number of such studies exist. Here, we analysed changes in the prevalence and abundance of gastrointestinal parasites in the wild Soay sheep population of St. Kilda across 31 years. The host population density (PD) has increased across the study, and PD is known to increase parasite transmission, but we found that PD and year explained temporal variation in parasite prevalence and abundance independently. Prevalence of both strongyle nematodes and coccidian microparasites increased during the study, and this effect varied between lambs, yearlings and adults. Meanwhile, abundance of strongyles was more strongly linked to host PD than to temporal (yearly) dynamics, while abundance of coccidia showed a strong temporal trend without any influence of PD. Strikingly, coccidian abundance increased 3-fold across the course of the study in lambs, while increases in yearlings and adults were negligible. Our decades-long, intensive, individual-based study will enable the role of environmental change and selection pressures in driving these dynamics to be determined, potentially providing unparalleled insight into the drivers of temporal variation in parasite dynamics in the wild.