Modeling undetected live type 1 wild poliovirus circulation after apparent interruption of transmission: Pakistan and Afghanistan.

Since 2013, wild poliovirus (WPV) transmission occurred only for type 1 (WPV1). Following several years of increasing reported incidence (2017-2019) and programmatic disruptions caused by COVID-19 (early 2020), Pakistan and Afghanistan performed a large number of supplementary immunization activities (late 2020-2021). This increased intensity of immunization, following widespread transmission, substantially decreased WPV1 cases and positive environmental samples during 2021. Modeling the potential for undetected circulation of WPV1 after apparent interruption can support regional and global decisions about certification of the eradication of indigenous WPV1 transmission. We apply a stochastic model to estimate the confidence about no circulation (CNC) of WPV1 in Pakistan and Afghanistan as a function of time since the last reported case and/or positive environmental sample. Exploration of different assumptions about surveillance quality suggests a range for CNC for WPV1 as a function of time since the last positive surveillance signal, and supports the potential use of a time with no evidence of transmission of less than 3 years as sufficient to assume die out in the context of good acute flaccid paralysis (AFP) surveillance. We show high expected CNC based on AFP surveillance data alone, even with imperfect surveillance and some use of inactivated poliovirus vaccine masking the ability of AFP surveillance to detect transmission. Ensuring high quality AFP and environmental surveillance may substantially shorten the time required to reach high CNC. The time required for high CNC depends on whether immunization activities maintain high population immunity and the quality of surveillance data.

Matching Methods to Problems: Using Data Science and Transmission Modeling to Combat Antimicrobial Resistance.

Antimicrobial resistance is a growing worldwide crisis, declared by the World Health Organization as "one of the principal threats to global public health today." The emergence and spread of antimicrobial resistance is a multifaceted problem that spans all aspects of healthcare, and research efforts to advance the field must likewise employ investigators with a diverse set of expertise and a variety of approaches and study designs who recognize and address the unique challenges of infectious-disease and antimicrobial-resistance research. An understanding of transmission dynamics and externalities, both positive and negative, is critical to any assessment of the impact of an intervention or policy related to infectious disease, infection prevention, or antimicrobial stewardship, in order to create a more comprehensive and accurate estimate of the costs and outcomes associated with an intervention. These types of advanced studies are necessary if we are to significantly alter the course of this crisis and improve the outlook for our future.

Modeling environmentally mediated rotavirus transmission: The role of temperature and hydrologic factors.

Rotavirus is considered a directly transmitted disease due to its high infectivity. Environmental pathways have, therefore, largely been ignored. Rotavirus, however, persists in water sources, and both its surface water concentrations and infection incidence vary with temperature. Here, we examine the potential for waterborne rotavirus transmission. We use a mechanistic model that incorporates both direct and waterborne transmission pathways, coupled with a hydrological model, and we simulate rotavirus transmission between two communities with interconnected water sources. To parameterize temperature dependency, we estimated temperature-dependent decay rates in water through a meta-analysis. Our meta-analysis suggests that rotavirus decay rates are positively associated with temperature (n = 39, P [Formula: see text] 0.001). This association is stronger at higher temperatures (over 20 °C), consistent with tropical climate conditions. Our model analysis demonstrates that water could disseminate rotavirus between the two communities for all modeled temperatures. While direct transmission was important for disease amplification within communities, waterborne transmission could also amplify transmission. In standing-water systems, the modeled increase in decay led to decreased disease, with every 1 °C increase in temperature leading to up to a 2.4% decrease in incidence. These effect sizes are consistent with prior meta-analyses, suggesting that environmental transmission through water sources may partially explain the observed associations between temperature and rotavirus incidence. Waterborne rotavirus transmission is likely most important in cooler seasons and in communities that use slow-moving or stagnant water sources. Even when indirect transmission through water cannot sustain outbreaks, it can seed outbreaks that are maintained by high direct transmission rates.

Modeling Undetected Live Poliovirus Circulation After Apparent Interruption of Transmission: Borno and Yobe in Northeast Nigeria.

Silent circulation of polioviruses complicates the polio endgame by affecting the confidence with which we can certify successful eradication (i.e., the end of transmission everywhere) given a long enough period of time with active surveillance and no observed detections. The Global Polio Eradication Initiative continues to use three years without observing paralytic cases caused by wild poliovirus (WPV) infection as an indication of sufficient confidence that poliovirus circulation stopped (assuming good surveillance). Prior modeling demonstrated the complexities of real populations and the imperfect nature of real surveillance systems, and highlighted the need for modeling the specific last reservoirs of undetected circulation. We use a poliovirus transmission model developed for Borno and Yobe to characterize the probability of undetected poliovirus circulation once apparent die-out occurs (i.e., in the absence of epidemiological signals) for WPV serotypes 1 and 3. Specifically, we convert the model to a stochastic form that supports estimates of confidence about no circulation given the time since the last detected event and considering the quality of both immunization and surveillance activities for these states. We find high confidence of no WPV3 circulation, and increasing confidence of WPV1 circulation, which we anticipate will imply high confidence in the absence of any detected cases in mid-2020 so long as Borno and Yobe maintain similar or achieve improved conditions. Our results confirm that gaps in poliovirus surveillance or reaching elimination with borderline sufficient population immunity can substantially increase the time to reach a high confidence about no undetected poliovirus transmission.

Environmental Surveillance System Characteristics and Impacts on Confidence About No Undetected Serotype 1 Wild Poliovirus Circulation.

Surveillance for poliovirus during the polio endgame remains uncertain. Building on prior modeling of the potential for undetected poliovirus transmission for conditions like those in Pakistan and Afghanistan, we use a hypothetical model to explore several key characteristics of the poliovirus environmental surveillance (ES) system (e.g., number and quality of sites, catchment sizes, and sampling frequency) and characterize their impacts on the time required to reach high (i.e., 95%) confidence about no circulation (CNC95%) following the last detected case of serotype 1 wild poliovirus. The nature and quality of the existing and future acute flaccid paralysis (AFP) surveillance and ES system significantly impact the estimated CNC95% for places like Pakistan and Afghanistan. The analysis illustrates the tradeoffs between number of sites, sampling frequency, and catchments sizes, and suggests diminishing returns of increasing these three factors beyond a point that depends on site quality and the location of sites. Limitations in data quality and the hypothetical nature of the model reduce the ability to assess the extent to which actual ES systems offer benefits that exceed their costs. Thus, although poliovirus ES may help to reduce the time required to reach high confidence about the absence of undetected circulation, the effect strongly depends on the ability to establish effective ES sites in high-risk areas. The costs and benefits of ES require further analysis.

Modeling Undetected Live Poliovirus Circulation After Apparent Interruption of Transmission: Pakistan and Afghanistan.

Since most poliovirus infections occur with no paralytic symptoms, the possibility of silent circulation complicates the confirmation of the end of poliovirus transmission. Based on empirical field experience and theoretical modeling results, the Global Polio Eradication Initiative identified three years without observing paralytic cases from wild polioviruses with good acute flaccid paralysis surveillance as an indication of sufficient confidence that poliovirus circulation stopped. The complexities of real populations and the imperfect nature of real surveillance systems subsequently demonstrated the importance of specific modeling for areas at high risk of undetected circulation, resulting in varying periods of time required to obtain the same level of confidence about no undetected circulation. Using a poliovirus transmission model that accounts for variability in transmissibility and neurovirulence for different poliovirus serotypes and characterizes country-specific factors (e.g., vaccination and surveillance activities, demographics) related to wild and vaccine-derived poliovirus transmission in Pakistan and Afghanistan, we consider the probability of undetected poliovirus circulation for those countries once apparent die-out occurs (i.e., in the absence of any epidemiological signals). We find that gaps in poliovirus surveillance or reaching elimination with borderline sufficient population immunity could significantly increase the time to reach high confidence about interruption of live poliovirus transmission, such that the path taken to achieve and maintain poliovirus elimination matters. Pakistan and Afghanistan will need to sustain high-quality surveillance for polioviruses after apparent interruption of transmission and recognize that as efforts to identify cases or circulating live polioviruses decrease, the risks of undetected circulation increase and significantly delay the global polio endgame.

On the Relative Role of Different Age Groups During Epidemics Associated With Respiratory Syncytial Virus.

Background: While circulation of respiratory syncytial virus (RSV) results in high rates of hospitalization, particularly among young children and elderly individuals, little is known about the role of different age groups in propagating annual RSV epidemics. Methods: We evaluate the roles played by individuals in different age groups during RSV epidemics in the United States between 2001 and 2012, using the previously defined relative risk (RR) statistic estimated from the hospitalization data from the Healthcare Cost and Utilization Project. Transmission modeling was used to examine the robustness of our inference method. Results: Children aged 3-4 years and 5-6 years each had the highest RR estimate for 5 of 11 seasons included in this study, with RSV hospitalization rates in infants being generally higher during seasons when children aged 5-6 years had the highest RR estimate. Children aged 2 years had the highest RR estimate during one season. RR estimates in infants and individuals aged ≥11 years were mostly lower than in children aged 1-10 years. Highest RR values aligned with groups for which vaccination had the largest impact on epidemic dynamics in most model simulations. Conclusions: Our estimates suggest the prominent relative roles of children aged ≤10 years (particularly among those aged 3-6 years) in propagating RSV epidemics. These results, combined with further modeling work, should help inform RSV vaccination policies.

Continental-scale, data-driven predictive assessment of eliminating the vector-borne disease, lymphatic filariasis, in sub-Saharan Africa by 2020.

BACKGROUND: There are growing demands for predicting the prospects of achieving the global elimination of neglected tropical diseases as a result of the institution of large-scale nation-wide intervention programs by the WHO-set target year of 2020. Such predictions will be uncertain due to the impacts that spatial heterogeneity and scaling effects will have on parasite transmission processes, which will introduce significant aggregation errors into any attempt aiming to predict the outcomes of interventions at the broader spatial levels relevant to policy making. We describe a modeling platform that addresses this problem of upscaling from local settings to facilitate predictions at regional levels by the discovery and use of locality-specific transmission models, and we illustrate the utility of using this approach to evaluate the prospects for eliminating the vector-borne disease, lymphatic filariasis (LF), in sub-Saharan Africa by the WHO target year of 2020 using currently applied or newly proposed intervention strategies. METHODS AND RESULTS: We show how a computational platform that couples site-specific data discovery with model fitting and calibration can allow both learning of local LF transmission models and simulations of the impact of interventions that take a fuller account of the fine-scale heterogeneous transmission of this parasitic disease within endemic countries. We highlight how such a spatially hierarchical modeling tool that incorporates actual data regarding the roll-out of national drug treatment programs and spatial variability in infection patterns into the modeling process can produce more realistic predictions of timelines to LF elimination at coarse spatial scales, ranging from district to country to continental levels. Our results show that when locally applicable extinction thresholds are used, only three countries are likely to meet the goal of LF elimination by 2020 using currently applied mass drug treatments, and that switching to more intensive drug regimens, increasing the frequency of treatments, or switching to new triple drug regimens will be required if LF elimination is to be accelerated in Africa. The proportion of countries that would meet the goal of eliminating LF by 2020 may, however, reach up to 24/36 if the WHO 1% microfilaremia prevalence threshold is used and sequential mass drug deliveries are applied in countries. CONCLUSIONS: We have developed and applied a data-driven spatially hierarchical computational platform that uses the discovery of locally applicable transmission models in order to predict the prospects for eliminating the macroparasitic disease, LF, at the coarser country level in sub-Saharan Africa. We show that fine-scale spatial heterogeneity in local parasite transmission and extinction dynamics, as well as the exact nature of intervention roll-outs in countries, will impact the timelines to achieving national LF elimination on this continent.

Modeling spatial invasion of Ebola in West Africa.

The 2014-2016 Ebola Virus Disease (EVD) epidemic in West Africa was the largest ever recorded, representing a fundamental shift in Ebola epidemiology with unprecedented spatiotemporal complexity. To understand the spatiotemporal dynamics of EVD in West Africa, we developed spatial transmission models using a gravity-model framework at both the national and district-level scales, which we used to compare effectiveness of local interventions (e.g. local quarantine) and long-range interventions (e.g. border-closures). The country-level gravity model captures the epidemic data, including multiple waves of initial epidemic growth observed in Guinea. We found that local-transmission reductions were most effective in Liberia, while long-range transmission was dominant in Sierra Leone. Both models illustrated that interventions in one region result in an amplified protective effect on other regions by preventing spatial transmission. In the district-level model, interventions in the strongest of these amplifying regions reduced total cases in all three countries by over 20%, in spite of the region itself generating only ∼0.1% of total cases. This model structure and associated intervention analysis provide information that can be used by public health policymakers to assist planning and response efforts for future epidemics.

Constructing Ebola transmission chains from West Africa and estimating model parameters using internet sources.

During the recent Ebola crisis in West Africa, individual person-level details of disease onset, transmissions, and outcomes such as survival or death were reported in online news media. We set out to document disease transmission chains for Ebola, with the goal of generating a timely account that could be used for surveillance, mathematical modeling, and public health decision-making. By accessing public web pages only, such as locally produced newspapers and blogs, we created a transmission chain involving two Ebola clusters in West Africa that compared favorably with other published transmission chains, and derived parameters for a mathematical model of Ebola disease transmission that were not statistically different from those derived from published sources. We present a protocol for responsibly gleaning epidemiological facts, transmission model parameters, and useful details from affected communities using mostly indigenously produced sources. After comparing our transmission parameters to published parameters, we discuss additional benefits of our method, such as gaining practical information about the affected community, its infrastructure, politics, and culture. We also briefly compare our method to similar efforts that used mostly non-indigenous online sources to generate epidemiological information.

Dynamic Vaccine Allocation for Control of Human-Transmissible Disease.

During pandemics, such as COVID-19, supplies of vaccines can be insufficient for meeting all needs, particularly when vaccines first become available. Our study develops a dynamic methodology for vaccine allocation, segmented by region, age, and timeframe, using a time-sensitive, age-structured compartmental model. Based on the objective of minimizing a weighted sum of deaths and cases, we used the Sequential Least Squares Quadratic Programming method to search for a locally optimal COVID-19 vaccine allocation for the United States, for the period from 16 December 2020 to 30 June 2021, where regions corresponded to the 50 states in the United States (U.S.). We also compared our solution to actual allocations of vaccines. From our model, we estimate that approximately 1.8 million cases and 9 thousand deaths could have been averted in the U.S. with an improved allocation. When case reduction is prioritized over death reduction, we found that young people (17 and younger) should receive priority over old people due to their potential to expose others. However, if death reduction is prioritized over case reduction, we found that more vaccines should be allocated to older people, due to their propensity for severe disease. While we have applied our methodology to COVID-19, our approach generalizes to other human-transmissible diseases, with potential application to future epidemics.

Bayesian modeling of dynamic behavioral change during an epidemic.

For many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, causing the transmission dynamics to change in real-time. Behavioral change is often ignored in epidemic modeling efforts, making these models less useful than they could be. We address this by introducing a novel class of data-driven epidemic models which characterize and accurately estimate behavioral change. Our proposed model allows time-varying transmission to be captured by the level of "alarm" in the population, with alarm specified as a function of the past epidemic trajectory. We investigate the estimability of the population alarm across a wide range of scenarios, applying both parametric functions and non-parametric functions using splines and Gaussian processes. The model is set in the data-augmented Bayesian framework to allow estimation on partially observed epidemic data. The benefit and utility of the proposed approach is illustrated through applications to data from real epidemics.

Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission Using a Hydrology-Based Model.

A combination of accelerated population growth and severe droughts has created pressure on food security and driven the development of irrigation schemes across sub-Saharan Africa. Irrigation has been associated with increased malaria risk, but risk prediction remains difficult due to the heterogeneity of irrigation and the environment. While investigating transmission dynamics is helpful, malaria models cannot be applied directly in irrigated regions as they typically rely only on rainfall as a source of water to quantify larval habitats. By coupling a hydrologic model with an agent-based malaria model for a sugarcane plantation site in Arjo, Ethiopia, we demonstrated how incorporating hydrologic processes to estimate larval habitats can affect malaria transmission. Using the coupled model, we then examined the impact of an existing irrigation scheme on malaria transmission dynamics. The inclusion of hydrologic processes increased the variability of larval habitat area by around two-fold and resulted in reduction in malaria transmission by 60%. In addition, irrigation increased all habitat types in the dry season by up to 7.4 times. It converted temporary and semi-permanent habitats to permanent habitats during the rainy season, which grew by about 24%. Consequently, malaria transmission was sustained all-year round and intensified during the main transmission season, with the peak shifted forward by around 1 month. Lastly, we evaluated the spatiotemporal distribution of adult vectors under the effect of irrigation by resolving habitat heterogeneity. These findings could help larval source management by identifying transmission hotspots and prioritizing resources for malaria elimination planning.

Analysing the impact of migration on HIV/AIDS cases using epidemiological modelling to guide policy makers.

In this paper, we present the impact of migration on the spread of HIV and AIDS cases. A simple model for HIV and AIDS that incorporates migration and addresses its contributions to the spread of HIV and AIDS cases was constructed. The model was calibrated to HIV and AIDS incidence data from Malaysia. We explore the use of Markov chain Monte Carlo (MCMC) simulation method to estimate uncertainty in all the unknown parameters incorporated in our proposed model. Among the migrant population, 1.5572e-01 were susceptible to HIV transmission, which constituted 67,801 migrants. A proportion of migrants, 6.3773e-04, were estimated to be HIV infected, constituting 278 migrants. There were 72 (per 10,000) migrants estimated to have had AIDS, representing a proportion of 1.6611e-08. The result suggests that the disease-free steady state was unstable since the estimated basic reproduction number R 0 was 2.0906 and 2.3322 for the models without and with migration, respectively. This is not a good indicator from the public health point of view, as the aim is to stabilize the epidemic at the disease-free equilibrium. The advantage of introduction of migration to the simple model validated the true R 0 and the transmission rate ß associated with HIV and AIDS epidemic disease in Malaysia. It also indicates an approximately 12 percentage points increase in the rate of HIV infection with migration.

Incorporating equity in infectious disease modeling: Case study of a distributional impact framework for measles transmission.

INTRODUCTION: Deterministic compartmental models of infectious diseases like measles typically reflect biological heterogeneities in the risk of infection and severity to characterize transmission dynamics. Given the known association of socioeconomic status and increased vulnerability to infection and mortality, it is also critical that such models further incorporate social heterogeneities. METHODS: Here, we aimed to explore the influence of integrating income-associated differences in parameters of traditional dynamic transmission models. We developed a measles SIR model, in which the Susceptible, Infected and Recovered classes were stratified by income quintile, with income-specific transmission rates, disease-induced mortality rates, and vaccination coverage levels. We further provided a stylized illustration with secondary data from Ethiopia, where we examined various scenarios demonstrating differences in transmission patterns by income and in distributional vaccination coverage, and quantified impacts on disparities in measles mortality. RESULTS: The income-stratified SIR model exhibited similar dynamics to that of the traditional SIR model, with amplified outbreak peaks and measles mortality among the poorest income group. All vaccination coverage strategies were found to substantially curb the overall number of measles deaths, yet most considerably for the poorest, with select strategies yielding clear reductions in measles mortality disparities. DISCUSSION: The incorporation of income-specific differences can reveal distinct outbreak patterns across income groups and important differences in the subsequent effects of preventative interventions like vaccination. Our case study highlights the need to extend traditional modeling frameworks (e.g. SIR models) to be stratified by socioeconomic factors like income and to consider ensuing income-associated differences in disease-related morbidity and mortality. In so doing, we build on existing tools and characterize ongoing challenges in achieving health equity.

The impact of implementing HIV prevention policies therapy and control strategy among HIV and AIDS incidence cases in Malaysia.

Malaysia is faced with a high HIV/AIDS burden that poses a public health threat. We constructed and applied a compartmental model to understand the spread and control of HIV/AIDS in Malaysia. A simple model for HIV and AIDS disease that incorporates condom and uncontaminated needle-syringes interventions and addresses the relative impact of given treatment therapy for infected HIV newborns on reducing HIV and AIDS incidence is presented. We demonstrated how treatment therapy for new-born babies and the use of condoms or uncontaminated needle-syringes impact the dynamics of HIV in Malaysia. The model was calibrated to HIV and AIDS incidence data from Malaysia from 1986 to 2011. The epidemiological parameters are estimated using Bayesian inference via Markov chain Monte Carlo simulation method. The reproduction number optimal for control of the HIV/AIDS disease obtained suggests that the disease-free equilibrium was unstable during the 25 years. However, the results indicated that the use of condoms and uncontaminated needle-syringes are pivotal intervention control strategies; a comprehensive adoption of the intervention may help stop the spread of HIV disease. Treatment therapy for newborn babies is also of high value; it reduces the epidemic peak. The combined effect of condom use or uncontaminated needle-syringe is more pronounced in controlling the spread of HIV/AIDS.

Quantitative Microbial Risk Assessment and Infectious Disease Transmission Modeling of Waterborne Enteric Pathogens.

PURPOSE OF REVIEW: Waterborne enteric pathogens remain a global health threat. Increasingly, quantitative microbial risk assessment (QMRA) and infectious disease transmission modeling (IDTM) are used to assess waterborne pathogen risks and evaluate mitigation. These modeling efforts, however, have largely been conducted independently for different purposes and in different settings. In this review, we examine the settings where each modeling strategy is employed. RECENT FINDINGS: QMRA research has focused on food contamination and recreational water in high-income countries (HICs) and drinking water and wastewater in low- and middle-income countries (LMICs). IDTM research has focused on large outbreaks (predominately LMICs) and vaccine-preventable diseases (LMICs and HICs). Human ecology determines the niches that pathogens exploit, leading researchers to focus on different risk assessment research strategies in different settings. To enhance risk modeling, QMRA and IDTM approaches should be integrated to include dynamics of pathogens in the environment and pathogen transmission through populations.

Development of a Taenia ovis transmission model and an assessment of control strategies.

The metacestode stage of the tapeworm, Taenia ovis, causes cystic lesions in the skeletal and cardiac muscle of sheep, which can result in the condemnation of the entire carcass. In recent years, Canadian farms have seen a marked increase in the number of condemnations due to T. ovis. Mathematical transmission models provide a useful tool for predicting parasite transmission and for evaluating the efficacy of potential control options. To date, no model has been developed exclusively for T. ovis. In the work described here, a compartmental, deterministic transmission model was developed to better understand the transmission dynamics of T. ovis on Canadian sheep farms. The model was intended to be practical, and represent the transmission of infection burdens in lambs that result in carcass condemnation, or transmission to canids. All transmission parameters were obtained from the literature or, when unavailable, expert opinion. The model incorporated each stage of the parasite lifecycle using the most probable transmission route on Canadian sheep farms; including definitive host (guard dogs), intermediate host (pastured lambs), and environment. Based on literature, the model performed as expected, and provided a reasonable estimate of parasite prevalence in lambs. In addition, modeling allowed the efficacy of potential control options to be evaluated and compared. Model simulations suggested that infection risk in market lambs could be eliminated through the regular treatment of guardian dogs every fifth week with an appropriate cestocide, or through eliminating carcass consumption by guardian dogs.