Analyzing the use of non-pharmaceutical personal protective measures through self-interest and social optimum for the control of an emerging disease.

Non-pharmaceutical personal protective (NPP) measures such as face masks use, and hand and respiratory hygiene can be effective measures for mitigating the spread of aerosol/airborne diseases, such as COVID-19, in the absence of vaccination or treatment. However, the usage of such measures is constrained by their inherent perceived cost and effectiveness for reducing transmission risk. To understand the complex interaction of disease dynamics and individuals decision whether to adopt NPP or not, we incorporate evolutionary game theory into an epidemic model such as COVID-19. To compare how self-interested NPP use differs from social optimum, we also investigated optional control from a central planner's perspective. We use Pontryagin's maximum principle to identify the population-level NPP uptake that minimizes disease incidence by incurring the minimum costs. The evolutionary behavior model shows that NPP uptake increases at lower perceived costs of NPP, higher transmission risk, shorter duration of NPP use, higher effectiveness of NPP, and shorter duration of disease-induced immunity. Though social optimum NPP usage is generally more effective in reducing disease incidence than self-interested usage, our analysis identifies conditions under which both strategies get closer. Our model provides new insights for public health in mitigating a disease outbreak through NPP.

Improved Assessment of Schistosoma Community Infection Through Data Resampling Method.

Background: The conventional diagnostic for Schistosoma mansoni infection is stool microscopy with the Kato-Katz (KK) technique to detect eggs. Its outcomes are highly variable on a day-to-day basis and may lead to biased estimates of community infection used to inform public health programs. Our goal is to develop a resampling method that leverages data from a large-scale randomized trial to accurately predict community infection. Methods: We developed a resampling method that provides unbiased community estimates of prevalence, intensity and other statistics for S mansoni infection when a community survey is conducted using KK stool microscopy with a single sample per host. It leverages a large-scale data set, collected in the Schistosomiasis Consortium for Operational Research and Evaluation (SCORE) project, and allows linking single-stool specimen community screening to its putative multiday "true statistics." Results: SCORE data analysis reveals the limited sensitivity of KK stool microscopy and systematic bias of single-day community testing versus multiday testing; for prevalence estimate, it can fall up to 50% below the true value. The proposed SCORE cluster method reduces systematic bias and brings the estimated prevalence values within 5%-10% of the true value. This holds for a broad swath of transmission settings, including SCORE communities, and other data sets. Conclusions: Our SCORE cluster method can markedly improve the S mansoni prevalence estimate in settings using stool microscopy.

Expansions to the MGDrivE suite for simulating the efficacy of novel gene-drive constructs in the control of mosquito-borne diseases.

OBJECTIVE: The MGDrivE (MGDrivE 1 and MGDrivE 2) modeling framework provides a flexible and expansive environment for testing the efficacy of novel gene-drive constructs for the control of mosquito-borne diseases. However, the existing model framework did not previously support several features necessary to simulate some types of intervention strategies. Namely, current MGDrivE versions do not permit modeling of small molecule inducible systems for controlling gene expression in gene drive designs or the inheritance patterns of self-eliminating gene drive mechanisms. RESULTS: Here, we demonstrate a new MGDrivE 2 module that permits the simulation of gene drive strategies incorporating small molecule-inducible systems and self-eliminating gene drive mechanisms. Additionally, we also implemented novel sparsity-aware sampling algorithms for improved computational efficiency in MGDrivE 2 and supplied an analysis and plotting function applicable to the outputs of MGDrivE 1 and MGDrivE 2.

Effectiveness of Systemic Insecticide Dog Treatment for the Control of Chagas Disease in the Tropics.

Chagas disease, caused by Trypanosoma cruzi and transmitted by triatomines, can lead to severe cardiac issues and mortality in many mammals. Recent studies have shown that systemic insecticide treatment of dogs is highly effective in killing triatomines. Here, we assessed the impact of dog treatment on T. cruzi transmission. We developed a mathematical model of T. cruzi transmission among triatomines, dogs, humans, and rodents. We used the model to evaluate the impact of dog treatment regimens on T. cruzi transmission dynamics to determine their effectiveness in reducing T. cruzi infection among hosts. We show that a 3-month treatment regimen may reduce T. cruzi incidence among humans by 59-80% in a high transmission setting, and 26-82% in a low transmission setting. An annual treatment may reduce incidence among humans by 49-74% in a high transmission setting, and by 11-76% in a low transmission setting. However, dog treatment may substantially increase T. cruzi prevalence among dogs if dog consumption of dead triatomines increases. Our model indicates that dog treatment may reduce T. cruzi infections among humans, but it may increase infections in dogs. Therefore, a holistic approach targeting different hosts is necessary for Chagas elimination.

A Scoping Review of Mathematical Models Used to Investigate the Role of Dogs in Chagas Disease Transmission.

Chagas disease is a zoonotic vector-borne disease caused by the parasite Trypanosoma cruzi, which affects a variety of mammalian species across the Americas, including humans and dogs. Mathematical modeling has been widely used to investigate the transmission dynamics and control of vector-borne diseases. We performed a scoping review of mathematical models that investigated the role of dogs in T. cruzi transmission. We identified ten peer-reviewed papers that have explicitly modeled the role of dogs in Chagas transmission dynamics. We discuss the different methods employed in these studies, the different transmission metrics, disease transmission routes, and disease control strategies that have been considered and evaluated. In general, mathematical modeling studies have shown that dogs are not only at high risk of T. cruzi infection but are also major contributors to T. cruzi transmission to humans. Moreover, eliminating infected dogs from households or frequent use of insecticide was shown to be effective for curtailing T. cruzi transmission in both humans and dogs. However, when insecticide spraying is discontinued, T. cruzi infections in dogs were shown to return to their pre-spraying levels. We discuss the challenges and opportunities for future modeling studies to improve our understanding of Chagas disease transmission dynamics and control.

Effectiveness of fluralaner treatment regimens for the control of canine Chagas disease: A mathematical modeling study.

BACKGROUND: Canine Chagas disease is caused by the protozoan parasite Trypanosoma cruzi and transmitted by insect triatomine vectors known as kissing bugs. The agent can cause cardiac damage and long-term heart disease and death in humans, dogs, and other mammals. In laboratory settings, treatment of dogs with systemic insecticides has been shown to be highly efficacious at killing triatomines that feed on treated dogs. METHOD: We developed compartmental vector-host models of T. cruzi transmission between the triatomine and dog population accounting for the impact of seasonality and triatomine migration on disease transmission dynamics. We considered a single vector-host model without seasonality, and model with seasonality, and a spatially coupled model. We used the models to evaluate the effectiveness of the insecticide fluralaner with different durations of treatment regimens for reducing T. cruzi infection in different transmission settings. RESULTS: In low and medium transmission settings, our model showed a marginal difference between the 3-month and 6-month regimens for reducing T. cruzi infection among dogs. The difference increases in the presence of seasonality and triatomine migration from a sylvatic transmission setting. In high transmission settings, the 3-month regimen was substantially more effective in reducing T. cruzi infections in dogs than the other regimens. Our model showed that increased migration rate reduces fluralaner effectiveness in all treatment regimens, but the relative reduction in effectiveness is minimal during the first years of treatment. However, if an additional 10% or more of triatomines killed by dog treatment were eaten by dogs, treatment could increase T. cruzi infections in the dog population at least during the first year of treatment. CONCLUSION: Our analysis shows that treating all peridomestic dogs every three to six months for at least five years could be an effective measure to reduce T. cruzi infections in dogs and triatomines in peridomestic transmission settings. However, further studies at the local scale are needed to better understand the potential impact of routine use of fluralaner treatment on increasing dogs' consumption of dead triatomines.

Evaluation of the US Definitions for Coronavirus Disease 2019 Community Risk Levels.

The US Centers for Disease Control and Prevention (CDC) defines a county metric of coronavirus disease 2019 (COVID-19) community levels to inform public health measures. We find that the COVID-19 community levels vary frequently over time, which may not be optimal for decision making. Alternative metric formulations that do not compromise predictive ability are shown to reduce variability.

Calibrating COVID-19 community transmission risk levels to reflect infection prevalence.

Many organizations, including the US Centers for Disease Control and Prevention, have developed risk indexes to help determine community transmission levels for the ongoing COVID-19 pandemic. These risk indexes are largely based on newly reported cases and percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests, which are well-established as biased estimates of COVID-19 transmission. However, transmission risk indexes should accurately and precisely communicate community risks to decision-makers and the public. Therefore, transmission risk indexes would ideally quantify actual, and not just reported, levels of disease prevalence or incidence. Here, we develop a robust data-driven framework for determining and communicating community transmission risk levels using reported cases and test positivity. We use this framework to evaluate the previous CDC community risk level metrics that were proposed as guidelines for determining COVID-19 transmission risk at community level in the US. Using two recently developed data-driven models for COVID-19 transmission in the US to compute community-level prevalence, we show that there is substantial overlap of prevalence between the different community risk levels from the previous CDC guidelines. Using our proposed framework, we redefined the risk levels and their threshold values. We show that these threshold values would have substantially reduced the overlaps of underlying community prevalence between counties/states in different community risk levels between 3/19/2020-9/9/2021. Our study demonstrates how the previous CDC community risk level indexes could have been calibrated to infection prevalence to improve their power to accurately determine levels of COVID-19 transmission in local communities across the US. This method can be used to inform the design of future COVID-19 transmission risk indexes.

Using test positivity and reported case rates to estimate state-level COVID-19 prevalence and seroprevalence in the United States.

Accurate estimates of infection prevalence and seroprevalence are essential for evaluating and informing public health responses and vaccination coverage needed to address the ongoing spread of COVID-19 in each United States (U.S.) state. However, reliable, timely data based on representative population sampling are unavailable, and reported case and test positivity rates are highly biased. A simple data-driven Bayesian semi-empirical modeling framework was developed and used to evaluate state-level prevalence and seroprevalence of COVID-19 using daily reported cases and test positivity ratios. The model was calibrated to and validated using published state-wide seroprevalence data, and further compared against two independent data-driven mathematical models. The prevalence of undiagnosed COVID-19 infections is found to be well-approximated by a geometrically weighted average of the positivity rate and the reported case rate. Our model accurately fits state-level seroprevalence data from across the U.S. Prevalence estimates of our semi-empirical model compare favorably to those from two data-driven epidemiological models. As of December 31, 2020, we estimate nation-wide a prevalence of 1.4% [Credible Interval (CrI): 1.0%-1.9%] and a seroprevalence of 13.2% [CrI: 12.3%-14.2%], with state-level prevalence ranging from 0.2% [CrI: 0.1%-0.3%] in Hawaii to 2.8% [CrI: 1.8%-4.1%] in Tennessee, and seroprevalence from 1.5% [CrI: 1.2%-2.0%] in Vermont to 23% [CrI: 20%-28%] in New York. Cumulatively, reported cases correspond to only one third of actual infections. The use of this simple and easy-to-communicate approach to estimating COVID-19 prevalence and seroprevalence will improve the ability to make public health decisions that effectively respond to the ongoing COVID-19 pandemic.

Forecasting influenza-like illness trends in Cameroon using Google Search Data.

Although acute respiratory infections are a leading cause of mortality in sub-Saharan Africa, surveillance of diseases such as influenza is mostly neglected. Evaluating the usefulness of influenza-like illness (ILI) surveillance systems and developing approaches for forecasting future trends is important for pandemic preparedness. We applied and compared a range of robust statistical and machine learning models including random forest (RF) regression, support vector machines (SVM) regression, multivariable linear regression and ARIMA models to forecast 2012 to 2018 trends of reported ILI cases in Cameroon, using Google searches for influenza symptoms, treatments, natural or traditional remedies as well as, infectious diseases with a high burden (i.e., AIDS, malaria, tuberculosis). The R2 and RMSE (Root Mean Squared Error) were statistically similar across most of the methods, however, RF and SVM had the highest average R2 (0.78 and 0.88, respectively) for predicting ILI per 100,000 persons at the country level. This study demonstrates the need for developing contextualized approaches when using digital data for disease surveillance and the usefulness of search data for monitoring ILI in sub-Saharan African countries.

Optimizing antiviral treatment for seasonal influenza in the USA: a mathematical modeling analysis.

BACKGROUND: Seasonal influenza remains a major cause of morbidity and mortality in the USA. Despite the US Centers for Disease Control and Prevention recommendation promoting the early antiviral treatment of high-risk patients, treatment coverage remains low. METHODS: To evaluate the population-level impact of increasing antiviral treatment timeliness and coverage among high-risk patients in the USA, we developed an influenza transmission model that incorporates data on infectious viral load, social contact, and healthcare-seeking behavior. We modeled the reduction in transmissibility in treated individuals based on their reduced daily viral load. The reduction in hospitalizations following treatment was based on estimates from clinical trials. We calibrated the model to weekly influenza data from Texas, California, Connecticut, and Virginia between 2014 and 2019. We considered in the baseline scenario that 2.7-4.8% are treated within 48 h of symptom onset while an additional 7.3-12.8% are treated after 48 h of symptom onset. We evaluated the impact of improving the timeliness and uptake of antiviral treatment on influenza cases and hospitalizations. RESULTS: Model projections suggest that treating high-risk individuals as early as 48 h after symptom onset while maintaining the current treatment coverage level would avert 2.9-4.5% of all symptomatic cases and 5.5-7.1% of all hospitalizations. Geographic variability in the effectiveness of earlier treatment arises primarily from variabilities in vaccination coverage and population demographics. Regardless of these variabilities, we found that when 20% of the high-risk individuals were treated within 48 h, the reduction in hospitalizations doubled. We found that treatment of the elderly population (> 65 years old) had the highest impact on reducing hospitalizations, whereas treating high-risk individuals aged 5-19 years old had the highest impact on reducing transmission. Furthermore, the population-level benefit per treated individual is enhanced under conditions of high vaccination coverage and a low attack rate during an influenza season. CONCLUSIONS: Increased timeliness and coverage of antiviral treatment among high-risk patients have the potential to substantially reduce the burden of seasonal influenza in the USA, regardless of influenza vaccination coverage and the severity of the influenza season.

State-level needs for social distancing and contact tracing to contain COVID-19 in the United States.

Starting in mid-May 2020, many US states began relaxing social-distancing measures that were put in place to mitigate the spread of COVID-19. To evaluate the impact of relaxation of restrictions on COVID-19 dynamics and control, we developed a transmission dynamic model and calibrated it to US state-level COVID-19 cases and deaths. We used this model to evaluate the impact of social distancing, testing and contact tracing on the COVID-19 epidemic in each state. As of 22 July 2020, we found that only three states were on track to curtail their epidemic curve. Thirty-nine states and the District of Columbia may have to double their testing and/or tracing rates and/or rolling back reopening by 25%, while eight states require an even greater measure of combined testing, tracing and distancing. Increased testing and contact-tracing capacity is paramount for mitigating the recent large-scale increases in US cases and deaths.

State-level needs for social distancing and contact tracing to contain COVID-19 in the United States.

Starting in mid-May 2020, many US states began relaxing social distancing measures that were put in place to mitigate the spread of COVID-19. To evaluate the impact of relaxation of restrictions on COVID-19 dynamics and control, we developed a transmission dynamic model and calibrated it to US state-level COVID-19 cases and deaths. We used this model to evaluate the impact of social distancing, testing and contact tracing on the COVID-19 epidemic in each state. As of July 22, 2020, we found only three states were on track to curtail their epidemic curve. Thirty-nine states and the District of Columbia may have to double their testing and/or tracing rates and/or rolling back reopening by 25%, while eight states require an even greater measure of combined testing, tracing, and distancing. Increased testing and contact tracing capacity is paramount for mitigating the recent large-scale increases in U.S. cases and deaths.

High Rate of Non-Human Feeding by Aedes aegypti Reduces Zika Virus Transmission in South Texas.

Mosquito-borne viruses are emerging or re-emerging globally, afflicting millions of people around the world. Aedes aegypti, the yellow fever mosquito, is the principal vector of dengue, Zika, and chikungunya viruses, and has well-established populations across tropical and subtropical urban areas of the Americas, including the southern United States. While intense arboviral epidemics have occurred in Mexico and further south in the Americas, local transmission in the United States has been minimal. Here, we study Ae. aegypti and Culex quinquefasciatus host feeding patterns and vertebrate host communities in residential environments of South Texas to identify host-utilization relative to availability. Only 31% of Ae. aegypti blood meals were derived from humans, while 50% were from dogs and 19% from other wild and domestic animals. In Cx. quinquefasciatus, 67% of blood meals were derived from chicken, 22% came from dogs, 9% from various wild avian species, and 2% from other mammals including one human, one cat, and one pig. We developed a model for the reproductive number, R0, for Zika virus (ZIKV) in South Texas relative to northern Mexico using human disease data from Tamaulipas, Mexico. We show that ZIKV R0 in South Texas communities could be greater than one if the risk of human exposure to Ae. aegypti bites in these communities is at least 60% that of Northern Mexico communities. The high utilization of non-human vertebrates and low risk of human exposure in South Texas diminishes the outbreak potential for human-amplified urban arboviruses transmitted by Ae. aegypti.

Assessing the impact of aggregating disease stage data in model predictions of human African trypanosomiasis transmission and control activities in Bandundu province (DRC).

Since the turn of the century, the global community has made great progress towards the elimination of gambiense human African trypanosomiasis (HAT). Elimination programs, primarily relying on screening and treatment campaigns, have also created a rich database of HAT epidemiology. Mathematical models calibrated with these data can help to fill remaining gaps in our understanding of HAT transmission dynamics, including key operational research questions such as whether integrating vector control with current intervention strategies is needed to achieve HAT elimination. Here we explore, via an ensemble of models and simulation studies, how including or not disease stage data, or using more updated data sets affect model predictions of future control strategies.

Spatio-temporal dynamics of measles outbreaks in Cameroon.

PURPOSE: In 2012, Cameroon experienced a large measles outbreak of over 14,000 cases. To determine the spatio-temporal dynamics of measles transmission in Cameroon, we analyzed weekly case data collected by the Ministry of Health. METHODS: We compared several multivariate time-series models of population movement to characterize the spatial spread of measles in Cameroon. Using the best model, we evaluated the contribution of population mobility to disease transmission at increasing geographic resolutions: region, department, and health district. RESULTS: Our spatio-temporal analysis showed that the power law model, which accounts for long-distance population movement, best represents the spatial spread of measles in Cameroon. Population movement between health districts within departments contributed to 7.6% (range: 0.4%-13.4%) of cases at the district level, whereas movement between departments within regions contributed to 16.0% (range: 1.3%-23.2%) of cases. Long-distance movement between regions contributed to 16.7% (range: 0.1%-59.0%) of cases at the region level, 20.1% (range: 7.1%-30.0%) at the department level, and 29.7% (range: 15.3%-47.6%) at the health district level. CONCLUSIONS: Population long-distance mobility is an important driver of measles dynamics in Cameroon. These findings demonstrate the need to improve our understanding of the roles of population mobility and local heterogeneity of vaccination coverage in the spread and control of measles in Cameroon.

Using Test Positivity and Reported Case Rates to Estimate State-Level COVID-19 Prevalence and Seroprevalence in the United States.

Accurate estimates of infection prevalence and seroprevalence are essential for evaluating and informing public health responses needed to address the ongoing spread of COVID-19 in the United States. A data-driven Bayesian single parameter semi-empirical model was developed and used to evaluate state-level prevalence and seroprevalence of COVID-19 using daily reported cases and test positivity ratios. COVID-19 prevalence is well-approximated by the geometric mean of the positivity rate and the reported case rate. As of December 8, 2020, we estimate nation-wide a prevalence of 1.4% [Credible Interval (CrI): 0.8%-1.9%] and a seroprevalence of 11.1% [CrI: 10.1%-12.2%], with state-level prevalence ranging from 0.3% [CrI: 0.2%-0.4%] in Maine to 3.0% [CrI: 1.1%-5.7%] in Pennsylvania, and seroprevalence from 1.4% [CrI: 1.0%-2.0%] in Maine to 22% [CrI: 18%-27%] in New York. The use of this simple and easy-to-communicate model will improve the ability to make public health decisions that effectively respond to the ongoing pandemic. BIOGRAPHICAL SKETCH OF AUTHORS: Dr. Weihsueh A. Chiu, is a professor of environmental health sciences at Texas A&M University. He is an expert in data-driven Bayesian modeling of public health related dynamical systems. Dr. Martial L. Ndeffo-Mbah, is an Assistant Professor of Epidemiology at Texas A&M University. He is an expert in mathematical and computational modeling of infectious diseases. SUMMARY LINE: Relying on reported cases and test positivity rates individually can result in incorrect inferences as to the spread of COVID-19, and public health decision-making can be improved by instead using their geometric mean as a measure of COVID-19 prevalence and transmission.

Global Risk and Elimination of Yellow Fever Epidemics.

BACKGROUND: Yellow fever (YF) is a vector-borne viral hemorrhagic disease endemic in Africa and Latin America. In 2016, the World Health Organization (WHO) developed the Eliminate YF Epidemics strategy aiming at eliminating YF epidemics by 2026. METHODS: We developed a spatiotemporal model of YF, accounting for the impact of temperature, vector distribution, and socioeconomic factors on disease transmission. We validated our model against previous estimates of YF basic reproductive number (R0). We used the model to estimate global risk of YF outbreaks and vaccination efforts needed to achieve elimination of YF epidemics. RESULTS: We showed that the global risk of YF outbreaks is highly heterogeneous. High-risk transmission areas (R0 > 6) are mainly found in West Africa and the Equatorial region of Latin America. We showed that vaccination coverage needed to eliminate YF epidemics in an endemic country varies substantially between districts. In many endemic countries, a 90% vaccination coverage is needed to achieve elimination. However, in some high-risk districts in Africa, a 95% coverage may be required. CONCLUSIONS: Global elimination of YF epidemics requires higher population-level immunity than the 80% coverage recommended by the WHO. Optimal YF vaccination strategy should be tailored to the risk profile of each endemic country.

The impact of vector migration on the effectiveness of strategies to control gambiense human African trypanosomiasis.

BACKGROUND: Several modeling studies have been undertaken to assess the feasibility of the WHO goal of eliminating gambiense human African trypanosomiasis (g-HAT) by 2030. However, these studies have generally overlooked the effect of vector migration on disease transmission and control. Here, we evaluated the impact of vector migration on the feasibility of interrupting transmission in different g-HAT foci. METHODS: We developed a g-HAT transmission model of a single tsetse population cluster that accounts for migration of tsetse fly into this population. We used a model calibration approach to constrain g-HAT incidence to ranges expected for high, moderate and low transmission settings, respectively. We used the model to evaluate the effectiveness of current intervention measures, including medical intervention through enhanced screening and treatment, and vector control, for interrupting g-HAT transmission in disease foci under each transmission setting. RESULTS: We showed that, in low transmission settings, under enhanced medical intervention alone, at least 70% treatment coverage is needed to interrupt g-HAT transmission within 10 years. In moderate transmission settings, a combination of medical intervention and a vector control measure with a daily tsetse mortality greater than 0.03 is required to achieve interruption of disease transmission within 10 years. In high transmission settings, interruption of disease transmission within 10 years requires a combination of at least 70% medical intervention coverage and at least 0.05 tsetse daily mortality rate from vector control. However, the probability of achieving elimination in high transmission settings decreases with an increased tsetse migration rate. CONCLUSION: Our results suggest that the WHO 2030 goal of G-HAT elimination is, at least in theory, achievable. But the presence of tsetse migration may reduce the probability of interrupting g-HAT transmission in moderate and high transmission foci. Therefore, optimal vector control programs should incorporate monitoring and controlling of vector density in buffer areas around foci of g-HAT control efforts.

The exacerbation of Ebola outbreaks by conflict in the Democratic Republic of the Congo.

The interplay between civil unrest and disease transmission is not well understood. Violence targeting healthcare workers and Ebola treatment centers in the Democratic Republic of the Congo (DRC) has been thwarting the case isolation, treatment, and vaccination efforts. The extent to which conflict impedes public health response and contributes to incidence has not previously been evaluated. We construct a timeline of conflict events throughout the course of the epidemic and provide an ethnographic appraisal of the local conditions that preceded and followed conflict events. Informed by temporal incidence and conflict data as well as the ethnographic evidence, we developed a model of Ebola transmission and control to assess the impact of conflict on the epidemic in the eastern DRC from April 30, 2018, to June 23, 2019. We found that both the rapidity of case isolation and the population-level effectiveness of vaccination varied notably as a result of preceding unrest and subsequent impact of conflict events. Furthermore, conflict events were found to reverse an otherwise declining phase of the epidemic trajectory. Our model framework can be extended to other infectious diseases in the same and other regions of the world experiencing conflict and violence.