Modeling the impact of xenointoxication in dogs to halt Trypanosoma cruzi transmission.

BACKGROUND: Chagas disease, a vector-borne parasitic disease caused by Trypanosoma cruzi, affects millions in the Americas. Dogs are important reservoirs of the parasite. Under laboratory conditions, canine treatment with the systemic insecticide fluralaner demonstrated efficacy in killing Triatoma infestans and T. brasiliensis, T. cruzi vectors, when they feed on dogs. This form of pest control is called xenointoxication. However, T. cruzi can also be transmitted orally when mammals ingest infected bugs, so there is potential for dogs to become infected upon consuming infected bugs killed by the treatment. Xenointoxication thereby has two contrasting effects on dogs: decreasing the number of insects feeding on the dogs but increasing opportunities for exposure to T. cruzi via oral transmission to dogs ingesting infected insects. OBJECTIVE: Examine the potential for increased infection rates of T. cruzi in dogs following xenointoxication. DESIGN/METHODS: We built a deterministic mathematical model, based on the Ross-MacDonald malaria model, to investigate the net effect of fluralaner treatment on the prevalence of T. cruzi infection in dogs in different epidemiologic scenarios. We drew upon published data on the change in percentage of bugs killed that fed on treated dogs over days post treatment. Parameters were adjusted to mimic three scenarios of T. cruzi transmission: high and low disease prevalence and domestic vectors, and low disease prevalence and sylvatic vectors. RESULTS: In regions with high endemic disease prevalence in dogs and domestic vectors, prevalence of infected dogs initially increases but subsequently declines before eventually rising back to the initial equilibrium following one fluralaner treatment. In regions of low prevalence and domestic or sylvatic vectors, however, treatment seems to be detrimental. In these regions our models suggest a potential for a rise in dog prevalence, due to oral transmission from dead infected bugs. CONCLUSION: Xenointoxication could be a beneficial and novel One Health intervention in regions with high prevalence of T. cruzi and domestic vectors. In regions with low prevalence and domestic or sylvatic vectors, there is potential harm. Field trials should be carefully designed to closely follow treated dogs and include early stopping rules if incidence among treated dogs exceeds that of controls.

Irrigation, migration and infestation: a case study of Chagas disease vectors and bed bugs in El Pedregal, Peru.

BACKGROUND: The city of El Pedregal grew out of a desert, following an agricultural irrigation project in southern Peru. OBJECTIVES: To describe infestation patterns by triatomines and bed bugs and their relationship to migration and urbanization. METHODS: We conducted door-to-door entomological surveys for triatomines and bed bugs. We assessed spatial clustering of infestations and compared the year of construction of infested to un-infested households. To gain a better understanding of the context surrounding triatomine infestations, we conducted in-depth interviews with residents to explore their migration histories, including previous experiences with infestation. FINDINGS: We inspected 5,164 households for Triatoma infestans (known locally as the Chirimacha); 21 (0.41%) were infested. These were extremely spatially clustered (Ripley's K p-value < 0.001 at various spatial scales). Infested houses were older than controls (Wilcoxon rank-sum: W = 33; p = 0.02). We conducted bed bug specific inspections in 34 households; 23 of these were infested. These were spatially dispersed across El Pedregal, and no difference was observed in construction age between bed bug infested houses and control houses (W = 6.5, p = 0.07). MAIN CONCLUSIONS: The establishment of agribusiness companies in a desert area demanded a permanent work force, leading to the emergence of a new city. Migrant farmers, seeking work opportunities or escaping from adverse climatic events, arrived with few resources, and constructed their houses with precarious materials. T. infestans, a Chagas disease vector, was introduced to the city and colonized houses, but its dispersal was constrained by presence of vacant houses. We discuss how changes in the socioeconomic and agricultural landscape can increase vulnerability to vector-borne illnesses.

Do Incentives Crowd Out Motivation? A Feasibility Study of a Community Vector-Control Campaign in Peru.

Incentives are a useful tool in encouraging healthy behavior as part of public health initiatives. However, there remains concern about motivation crowd out-a decline in levels of motivation to undertake a behavior to below baseline levels after incentives have been removed-and few public health studies have assessed for motivation crowd out. Here, we assess the feasibility of identifying motivation crowd out following a lottery to promote participation in a Chagas disease vector control campaign. We look for evidence of crowd out in subsequent participation in the same behavior, a related behavior, and an unrelated behavior. We identified potential motivation crowd out for the same behavior, but not for related behavior or unrelated behaviors after lottery incentives are removed. Despite some limitations, we conclude that motivation crowd out is feasible to assess in large-scale trials of incentives.

Dynamics of COVID-19 under social distancing measures are driven by transmission network structure.

In the absence of pharmaceutical interventions, social distancing is being used worldwide to curb the spread of COVID-19. The impact of these measures has been inconsistent, with some regions rapidly nearing disease elimination and others seeing delayed peaks or nearly flat epidemic curves. Here we build a stochastic epidemic model to examine the effects of COVID-19 clinical progression and transmission network structure on the outcomes of social distancing interventions. Our simulations show that long delays between the adoption of control measures and observed declines in cases, hospitalizations, and deaths occur in many scenarios. We find that the strength of within-household transmission is a critical determinant of success, governing the timing and size of the epidemic peak, the rate of decline, individual risks of infection, and the success of partial relaxation measures. The structure of residual external connections, driven by workforce participation and essential businesses, interacts to determine outcomes. We suggest limited conditions under which the formation of household "bubbles" can be safe. These findings can improve future predictions of the timescale and efficacy of interventions needed to control second waves of COVID-19 as well as other similar outbreaks, and highlight the need for better quantification and control of household transmission.

Dynamics of bed bug infestations and control under disclosure policies.

Bed bugs have reemerged in the United States and worldwide over recent decades, presenting a major challenge to both public health practitioners and housing authorities. A number of municipalities have proposed or initiated policies to stem the bed bug epidemic, but little guidance is available to evaluate them. One contentious policy is disclosure, whereby landlords are obligated to notify potential tenants of current or prior bed bug infestations. Aimed to protect tenants from leasing an infested rental unit, disclosure also creates a kind of quarantine, partially and temporarily removing infested units from the market. Here, we develop a mathematical model for the spread of bed bugs in a generalized rental market, calibrate it to parameters of bed bug dispersion and housing turnover, and use it to evaluate the costs and benefits of disclosure policies to landlords. We find disclosure to be an effective control policy to curb infestation prevalence. Over the short term (within 5 years), disclosure policies result in modest increases in cost to landlords, while over the long term, reductions of infestation prevalence lead, on average, to savings. These results are insensitive to different assumptions regarding the prevalence of infestation, rate of introduction of bed bugs from other municipalities, and the strength of the quarantine effect created by disclosure. Beyond its application to bed bugs, our model offers a framework to evaluate policies to curtail the spread of household pests and is appropriate for systems in which spillover effects result in highly nonlinear cost-benefit relationships.

Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations. OBJECTIVE: To estimate the timing of surges in clinical demand and the best- and worst-case scenarios of local COVID-19-induced strain on hospital capacity, and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated. DESIGN: Monte Carlo simulation instantiation of a susceptible, infected, removed (SIR) model with a 1-day cycle. SETTING: 3 hospitals in an academic health system. PATIENTS: All people living in the greater Philadelphia region. MEASUREMENTS: The COVID-19 Hospital Impact Model (CHIME) (http://penn-chime.phl.io) SIR model was used to estimate the time from 23 March 2020 until hospital capacity would probably be exceeded, and the intensity of the surge, including for intensive care unit (ICU) beds and ventilators. RESULTS: Using patients with COVID-19 alone, CHIME estimated that it would be 31 to 53 days before demand exceeds existing hospital capacity. In best- and worst-case scenarios of surges in the number of patients with COVID-19, the needed total capacity for hospital beds would reach 3131 to 12 650 across the 3 hospitals, including 338 to 1608 ICU beds and 118 to 599 ventilators. LIMITATIONS: Model parameters were taken directly or derived from published data across heterogeneous populations and practice environments and from the health system's historical data. CHIME does not incorporate more transition states to model infection severity, social networks to model transmission dynamics, or geographic information to account for spatial patterns of human interaction. CONCLUSION: Publicly available and designed for hospital operations leaders, this modeling tool can inform preparations for capacity strain during the early days of a pandemic. PRIMARY FUNDING SOURCE: University of Pennsylvania Health System and the Palliative and Advanced Illness Research Center.

A behavioral design approach to improving a Chagas disease vector control campaign in Peru.

BACKGROUND: Individual behavior change is a critical ingredient in efforts to improve global health. Central to the focus on behavior has been a growing understanding of how the human brain makes decisions, from motivations and mindsets to unconscious biases and cognitive shortcuts. Recent work in the field of behavioral economics and related fields has contributed to a rich menu of insights and principles that can be engineered into global health programs to increase impact and reach. However, there is little research on the process of designing and testing interventions informed by behavioral insights. METHODS: In a study focused on increasing household participation in a Chagas disease vector control campaign in Arequipa, Peru, we applied Datta and Mullainathan's "behavioral design" approach to formulate and test specific interventions. In this Technical Advance article we describe the behavioral design approach in detail, including the Define, Diagnosis, Design, and Test phases. We also show how the interventions designed through the behavioral design process were adapted for a pragmatic randomized controlled field trial. RESULTS: The behavioral design framework provided a systematic methodology for defining the behavior of interest, diagnosing reasons for household reluctance or refusal to participate, designing interventions to address actionable bottlenecks, and then testing those interventions in a rigorous counterfactual context. Behavioral design offered us a broader range of strategies and approaches than are typically used in vector control campaigns. CONCLUSIONS: Careful attention to how behavioral design may affect internal and external validity of evaluations and the scalability of interventions is needed going forward. We recommend behavioral design as a useful complement to other intervention design and evaluation approaches in global health programs.

Two-scale dispersal estimation for biological invasions via synthetic likelihood.

Biological invasions reshape environments and affect the ecological and economic welfare of states and communities. Such invasions advance on multiple spatial scales, complicating their control. When modeling stochastic dispersal processes, intractable likelihoods and autocorrelated data complicate parameter estimation. As with other approaches, the recent synthetic likelihood framework for stochastic models uses summary statistics to reduce this complexity; however, it additionally provides usable likelihoods, facilitating the use of existing likelihood-based machinery. Here, we extend this framework to parameterize multi-scale spatio-temporal dispersal models and compare existing and newly developed spatial summary statistics to characterize dispersal patterns. We provide general methods to evaluate potential summary statistics and present a fitting procedure that accurately estimates dispersal parameters on simulated data. Finally, we apply our methods to quantify the short and long range dispersal of Chagas disease vectors in urban Arequipa, Peru, and assess the feasibility of a purely reactive strategy to contain the invasion.

Quantitative methods of identifying the key nodes in the illegal wildlife trade network.

Innovative approaches are needed to combat the illegal trade in wildlife. Here, we used network analysis and a new database, HealthMap Wildlife Trade, to identify the key nodes (countries) that support the illegal wildlife trade. We identified key exporters and importers from the number of shipments a country sent and received and from the number of connections a country had to other countries over a given time period. We used flow betweenness centrality measurements to identify key intermediary countries. We found the set of nodes whose removal from the network would cause the maximum disruption to the network. Selecting six nodes would fragment 89.5% of the network for elephants, 92.3% for rhinoceros, and 98.1% for tigers. We then found sets of nodes that would best disseminate an educational message via direct connections through the network. We would need to select 18 nodes to reach 100% of the elephant trade network, 16 nodes for rhinoceros, and 10 for tigers. Although the choice of locations for interventions should be customized for the animal and the goal of the intervention, China was the most frequently selected country for network fragmentation and information dissemination. Identification of key countries will help strategize illegal wildlife trade interventions.

Seasons, Searches, and Intentions: What The Internet Can Tell Us About The Bed Bug (Hemiptera: Cimicidae) Epidemic.

The common bed bug (Cimex lectularius L.) is once again prevalent in the United States. We investigated temporal patterns in Google search queries for bed bugs and co-occurring terms, and conducted in-person surveys to explore the intentions behind searches that included those terms. Searches for "bed bugs" rose steadily through 2011 and then plateaued, suggesting that the epidemic has reached an equilibrium in the United States. However, queries including terms that survey respondents associated strongly with having bed bugs (e.g., "exterminator," "remedies") continued to climb, while terms more closely associated with informational searches (e.g., "hotels," "about") fell. Respondents' rankings of terms and nonseasonal trends in Google search volume as assessed by a cosinor model were significantly correlated (Kendall's Tau-b P = 0.015). We find no evidence from Google Trends that the bed bug epidemic in the United States has reached equilibrium.

Bottlenecks in domestic animal populations can facilitate the emergence of Trypanosoma cruzi, the aetiological agent of Chagas disease.

Faeces-mediated transmission of Trypanosoma cruzi (the aetiological agent of Chagas disease) by triatomine insects is extremely inefficient. Still, the parasite emerges frequently, and has infected millions of people and domestic animals. We synthesize here the results of field and laboratory studies of T. cruzi transmission conducted in and around Arequipa, Peru. We document the repeated occurrence of large colonies of triatomine bugs (more than 1000) with very high infection prevalence (more than 85%). By inoculating guinea pigs, an important reservoir of T. cruzi in Peru, and feeding triatomine bugs on them weekly, we demonstrate that, while most animals quickly control parasitaemia, a subset of animals remains highly infectious to vectors for many months. However, we argue that the presence of these persistently infectious hosts is insufficient to explain the observed prevalence of T. cruzi in vector colonies. We posit that seasonal rains, leading to a fluctuation in the price of guinea pig food (alfalfa), leading to annual guinea pig roasts, leading to a concentration of vectors on a small subpopulation of animals maintained for reproduction, can propel T. cruzi through vector colonies and create a considerable force of infection for a pathogen whose transmission might otherwise fizzle out.

Public health impact of strain specific immunity to Borrelia burgdorferi.

BACKGROUND: Lyme disease, caused by Borrelia burgdorferi, is the most common tick-borne infection in the United States. Although humans can be infected by at least 16 different strains of B. burgdorferi, the overwhelming majority of infections are due to only four strains. It was recently demonstrated that patients who are treated for early Lyme disease develop immunity to the specific strain of B. burgdorferi that caused their infection. The aim of this study is to estimate the reduction in cases of Lyme disease in the United States that may occur as a result of type specific immunity. METHODS: The analysis was performed based on three analytical models that assessed the effects of type specific immunity. Observational data on the frequency with which different B. burgdorferi strains cause human infection in culture-confirmed patients with an initial episode of erythema migrans diagnosed between 1991 and 2005 in the Northeastern United States were used in the analyses. RESULTS: Assuming a reinfection rate of 3 % and a total incidence of Lyme disease per year of 300,000, the estimated number of averted cases of Lyme disease per year ranges from 319 to 2378 depending on the duration of type specific immunity and the model used. CONCLUSION: Given the assumptions of the analyses, this analysis suggests that type specific immunity is likely to have public health significance in the United States.

Residual infestation and recolonization during urban Triatoma infestans Bug Control Campaign, Peru.

Chagas disease vector control campaigns are being conducted in Latin America, but little is known about medium-term or long-term effectiveness of these efforts, especially in urban areas. After analyzing entomologic data for 56,491 households during the treatment phase of a Triatoma infestans bug control campaign in Arequipa, Peru, during 2003-2011, we estimated that 97.1% of residual infestations are attributable to untreated households. Multivariate models for the surveillance phase of the campaign obtained during 2009-2012 confirm that nonparticipation in the initial treatment phase is a major risk factor (odds ratio [OR] 21.5, 95% CI 3.35-138). Infestation during surveillance also increased over time (OR 1.55, 95% CI 1.15-2.09 per year). In addition, we observed a negative interaction between nonparticipation and time (OR 0.73, 95% CI 0.53-0.99), suggesting that recolonization by vectors progressively dilutes risk associated with nonparticipation. Although the treatment phase was effective, recolonization in untreated households threatens the long-term success of vector control.

Colonization of Cimex lectularius with methicillin-resistant Staphylococcus aureus.

A recent paper published by Lowe and Romney in Emerging Infectious Diseases titled, Bed bugs as Vectors for Drug-Resistant Bacteria has sparked a renewed interest in bed bug vector potential. We followed a pyrethroid resistant strain of the human bed bug (Cimex lectularius, L.) fed either human blood or human blood with added methicillin resistant Staphylococcus aureus (MRSA) for 9 days post-feeding. Results indicated that while the bed bug midgut is a hospitable environment for MRSA, the bacteria does not survive longer than 9 days within the midgut. Additionally, MRSA is not amplified within the midgut of the bug as the infection was cleared within 9 days. Due to the weekly feeding behaviours of bed bugs, these results suggest that bed bug transmission of MRSA is highly unlikely.

Urbanization, land tenure security and vector-borne Chagas disease.

Modern cities represent one of the fastest growing ecosystems on the planet. Urbanization occurs in stages; each stage characterized by a distinct habitat that may be more or less susceptible to the establishment of disease vector populations and the transmission of vector-borne pathogens. We performed longitudinal entomological and epidemiological surveys in households along a 1900 × 125 m transect of Arequipa, Peru, a major city of nearly one million inhabitants, in which the transmission of Trypanosoma cruzi, the aetiological agent of Chagas disease, by the insect vector Triatoma infestans, is an ongoing problem. The transect spans a cline of urban development from established communities to land invasions. We find that the vector is tracking the development of the city, and the parasite, in turn, is tracking the dispersal of the vector. New urbanizations are free of vector infestation for decades. T. cruzi transmission is very recent and concentrated in more established communities. The increase in land tenure security during the course of urbanization, if not accompanied by reasonable and enforceable zoning codes, initiates an influx of construction materials, people and animals that creates fertile conditions for epidemics of some vector-borne diseases.

The effects of city streets on an urban disease vector.

With increasing urbanization vector-borne diseases are quickly developing in cities, and urban control strategies are needed. If streets are shown to be barriers to disease vectors, city blocks could be used as a convenient and relevant spatial unit of study and control. Unfortunately, existing spatial analysis tools do not allow for assessment of the impact of an urban grid on the presence of disease agents. Here, we first propose a method to test for the significance of the impact of streets on vector infestation based on a decomposition of Moran's spatial autocorrelation index; and second, develop a Gaussian Field Latent Class model to finely describe the effect of streets while controlling for cofactors and imperfect detection of vectors. We apply these methods to cross-sectional data of infestation by the Chagas disease vector Triatoma infestans in the city of Arequipa, Peru. Our Moran's decomposition test reveals that the distribution of T. infestans in this urban environment is significantly constrained by streets (p<0.05). With the Gaussian Field Latent Class model we confirm that streets provide a barrier against infestation and further show that greater than 90% of the spatial component of the probability of vector presence is explained by the correlation among houses within city blocks. The city block is thus likely to be an appropriate spatial unit to describe and control T. infestans in an urban context. Characteristics of the urban grid can influence the spatial dynamics of vector borne disease and should be considered when designing public health policies.

Spatial and temporal patterns in Cimex lectularius (Hemiptera: Cimicidae) reporting in Philadelphia, PA.

We analyzed a comprehensive telephone log of pest infestation reports to assess the spatial and temporal trends in Cimex lectularius L. (bed bug) reporting throughout Philadelphia, PA. Citywide spatial analyses of reports from September 2011 to June 2012 revealed several statistically significant bed bug hotspots. However, these were small and diffuse. Temporal analyses of reports from December 2008 to May 2011 detected prominent seasonality in bed bug reporting, peaking in August and reaching a nadir in February each year. Controlling for seasonal cycling, the number of bed bug reports in Philadelphia increased steadily at a rate of approximately 4.5% per month (or 69.45% per year) from December 2008 to May 2011. While it may be difficult to spatially target citywide bed bug control measures because of the insects' widespread migration, interventions informed by seasonal trends may enhance efforts to curb the recent increases in urban bed bug populations.

Enhancing Mass Vaccination Programs with Queueing Theory and Spatial Optimization.

Background: Mass vaccination is a cornerstone of public health emergency preparedness and response. However, injudicious placement of vaccination sites can lead to the formation of long waiting lines or queues, which discourages individuals from waiting to be vaccinated and may thus jeopardize the achievement of public health targets. Queueing theory offers a framework for modeling queue formation at vaccination sites and its effect on vaccine uptake. Methods: We developed an algorithm that integrates queueing theory within a spatial optimization framework to optimize the placement of mass vaccination sites. The algorithm was built and tested using data from a mass canine rabies vaccination campaign in Arequipa, Peru. We compared expected vaccination coverage and losses from queueing (i.e., attrition) for sites optimized with our queue-conscious algorithm to those obtained from a queue-naive version of the same algorithm. Results: Sites placed by the queue-conscious algorithm resulted in 9-19% less attrition and 1-2% higher vaccination coverage compared to sites placed by the queue-naïve algorithm. Compared to the queue-naïve algorithm, the queue-conscious algorithm favored placing more sites in densely populated areas to offset high arrival volumes, thereby reducing losses due to excessive queueing. These results were not sensitive to misspecification of queueing parameters or relaxation of the constant arrival rate assumption. Conclusion: One should consider losses from queueing to optimally place mass vaccination sites, even when empirically derived queueing parameters are not available. Due to the negative impacts of excessive wait times on participant satisfaction, reducing queueing attrition is also expected to yield downstream benefits and improve vaccination coverage in subsequent mass vaccination campaigns.

Genomic Characterization of a Dog-Mediated Rabies Outbreak in El Pedregal, Arequipa, Peru.

Background: Rabies, a re-emerging zoonosis with the highest known human case fatality rate, has been largely absent from Peru, except for endemic circulation in the Puno region on the Bolivian border and re-emergence in Arequipa City in 2015, where it has persisted. In 2021, an outbreak occurred in the rapidly expanding city of El Pedregal near Arequipa, followed by more cases in 2022 after nearly a year of epidemiological silence. While currently under control, questions persist regarding the origin of the El Pedregal outbreak and implications for maintaining rabies control in Peru. Methods: We sequenced 25 dog rabies virus (RABV) genomes from the El Pedregal outbreak (n=11) and Arequipa City (n=14) from 2021-2023 using Nanopore sequencing in Peru. Historical genomes from Puno (n=4, 2010-2012) and Arequipa (n=5, 2015-2019), were sequenced using an Illumina approach in the UK. In total, 34 RABV genomes were analyzed, including archived and newly obtained samples. The genomes were analyzed phylogenetically to understand the outbreak's context and origins. Results: Phylogenomic analysis identified two genetic clusters in El Pedregal: 2021 cases stemmed from a single introduction unrelated to Arequipa cases, while the 2022 sequence suggested a new introduction from Arequipa rather than persistence. In relation to canine RABV diversity in Latin America, all new sequences belonged to a new minor clade, Cosmopolitan Am5, sharing relatives from Bolivia, Argentina, and Brazil. Conclusion: Genomic insights into the El Pedregal outbreak revealed multiple introductions over a 2-year window. Eco-epidemiological conditions, including migratory worker patterns, suggest human-mediated movement drove introductions. Despite outbreak containment, El Pedregal remains at risk of dog-mediated rabies due to ongoing circulation in Arequipa, Puno, and Bolivia. Human-mediated movement of dogs presents a major risk for rabies re-emergence in Peru, jeopardizing regional dog-mediated rabies control. Additional sequence data is needed for comprehensive phylogenetic analyses.

Optimizing the location of vaccination sites to stop a zoonotic epidemic.

Mass vaccinations are crucial public health interventions for curbing infectious diseases. Canine rabies control relies on mass dog vaccination campaigns (MDVCs) that are held annually across the globe. Dog owners must bring their pets to fixed vaccination sites, but sometimes target coverage is not achieved due to low participation. Travel distance to vaccination sites is an important barrier to participation. We aimed to increase MDVC participation in silico by optimally placing fixed-point vaccination locations. We quantified participation probability based on walking distance to the nearest vaccination site using regression models fit to participation data collected over 4 years. We used computational recursive interchange techniques to optimally place fixed-point vaccination sites and compared predicted participation with these optimally placed vaccination sites to actual locations used in previous campaigns. Algorithms that minimized average walking distance or maximized expected participation provided the best solutions. Optimal vaccination placement is expected to increase participation by 7% and improve spatial evenness of coverage, resulting in fewer under-vaccinated pockets. However, unevenness in workload across sites remained. Our data-driven algorithm optimally places limited resources to increase overall vaccination participation and equity. Field evaluations are essential to assess effectiveness and evaluate potentially longer waiting queues resulting from increased participation.