Assessment of using Google Trends for real-time monitoring of infectious disease outbreaks: a measles case study.

Measles remains a significant threat to children worldwide despite the availability of effective vaccines. The COVID-19 pandemic exacerbated the situation by leading to the postponement of supplementary measles immunization activities. Along with this postponement, measles surveillance also deteriorated, with the lowest number of submitted specimens in over a decade. In this study, we focus on measles as a challenging case study due to its high vaccination coverage, which leads to smaller outbreaks and potentially weaker signals on Google Trends. Our research aimed to explore the feasibility of using Google Trends for real-time monitoring of infectious disease outbreaks. We evaluated the correlation between Google Trends searches and clinical case data using the Pearson correlation coefficient and Spearman's rank correlation coefficient across 30 European countries and Japan. The results revealed that Google Trends was most suitable for monitoring acute disease outbreaks at the regional level in high-income countries, even when there are only a few weekly cases. For example, from 2017 to 2019, the Pearson correlation coefficient was 0.86 (p-value< 0.05) at the prefecture level for Okinawa, Japan, versus 0.33 (p-value< 0.05) at the national level for Japan. Furthermore, we found that the Pearson correlation coefficient may be more suitable than Spearman's rank correlation coefficient for evaluating the correlations between Google Trends search data and clinical case data. This study highlighted the potential of utilizing Google Trends as a valuable tool for timely public health interventions to respond to infectious disease outbreaks, even in the context of diseases with high vaccine coverage.

Comparison of a static cohort model and dynamic transmission model for respiratory syncytial virus intervention programs for infants in England and Wales.

BACKGROUND: A recent study comparing results of multiple cost-effectiveness analyses (CEAs) in a hypothetical population found that monoclonal antibody (mAb) immunoprophylaxis for respiratory syncytial virus (RSV) in infants averted fewer medically attended cases when estimated using dynamic transmission models (DTMs) versus static cohort models (SCMs). We aimed to investigate whether model calibration or parameterization could be the primary driver of inconsistencies between SCM and DTM predictions. METHODS: A recently published DTM evaluating the CEA of infant mAb immunoprophylaxis in England and Wales (EW) was selected as the reference model. We adapted our previously published SCM for US infants to EW by utilizing the same data sources used by the DTM. Both models parameterized mAb efficacy from a randomized clinical trial (RCT) that estimated an average efficacy of 74.5% against all medically attended RSV episodes and 62.1% against RSV hospitalizations. To align model assumptions, we modified the SCM to incorporate waning efficacy. Since the estimated indirect effects from the DTM were small (i.e., approximately 100-fold smaller in magnitude than direct effects), we hypothesized that alignment of model parameters should result in alignment of model predictions. Outputs for model comparison comprised averted hospitalizations and averted GP visits, estimated for seasonal (S) and seasonal-with-catchup (SC) immunization strategies. RESULTS: When we aligned the SCM intervention parameters to DTM intervention parameters, significantly more averted hospitalizations were predicted by the SCM (S: 32.3%; SC: 51.3%) than the DTM (S: 17.8%; SC: 28.6%). The SCM most closely replicated the DTM results when the initial efficacy of the mAb intervention was 62.1%, leading to an average efficacy of 39.3%. Under this parameterization the SCM predicted 17.4% (S) and 27.7% (SC) averted hospitalizations. Results were similar for averted GP visits. CONCLUSIONS: Parameterization of the RSV mAb intervention efficacy is a plausible primary driver of differences between SCM versus DTM model predictions.

Systematic review of rotavirus vaccination cost-effectiveness in high income settings utilising dynamic transmission modelling techniques.

PURPOSE: This systematic review presents cost-effectiveness studies of rotavirus vaccination in high-income settings based on dynamic transmission modelling to inform policy decisions about implementing rotavirus vaccination programmes. METHODS: We searched CEA Registry, MEDLINE, Embase, Health Technology Assessment Database, Scopus, and the National Health Service Economic Evaluation Database for studies published since 2002. Full economic evaluation studies based on dynamic transmission models, focusing on high-income countries, live oral rotavirus vaccine and children ≤ 5 years of age were eligible for inclusion. Included studies were appraised for quality and risk of bias using the Consensus on Health Economic Criteria (CHEC) list and the Philips checklist. The review protocol was prospectively registered with PROSPERO (CRD42020208406). RESULTS: A total of four economic evaluations were identified. Study settings included England and Wales, France, Norway, and the United States. All studies compared either pentavalent or monovalent rotavirus vaccines to no intervention. All studies were cost-utility analyses that reported incremental cost per quality-adjusted life year (QALY) gained. Included studies consistently concluded that rotavirus vaccination is cost-effective compared with no vaccination relative to the respective country's willingness to pay threshold when herd protection benefits are incorporated in the modelling framework. CONCLUSIONS: Rotavirus vaccination was found to be cost-effective in all identified studies that used dynamic transmission models in high-income settings where child mortality rates due to rotavirus gastroenteritis are close to zero. Previous systematic reviews of economic evaluations considered mostly static models and had less conclusive findings than the current study. This review suggests that modelling choices influence cost-effectiveness results for rotavirus vaccination. Specifically, the review suggests that dynamic transmission models are more likely to account for the full impact of rotavirus vaccination than static models in cost-effectiveness analyses.

Optimizing age specific strategies of vaccination for prevention of cytomegalovirus infection in the US using agent-based simulation.

BACKGROUND: There is an urgent need to develop a cytomegalovirus (CMV) vaccine as it remains the leading cause of birth defects in the United States. While several CMV vaccine candidates are currently in late-stage clinical trials, the most effective vaccination program remains an open research question. METHODS: To take into account the critical uncertainties when evaluating the vaccine impact on both vertical (congenital) and horizontal CMV transmissions, we developed a CMV agent-based model representative of the US population and contact network structures. RESULTS: We evaluated 648 vaccination scenarios under various assumptions of vaccination age, vaccine efficacy, protection duration, and vaccination coverage. The optimal age of vaccination under all scenarios is shown to be during early childhood. However, a relatively modest benefit was also seen with vaccination of females of reproduction age (around age of 25) assuming near universal coverage and long vaccine-mediated protection. CONCLUSIONS: This study highlights the important need for a pediatric vaccination program in mitigating CMV in the United States. Our model is poised to investigate further location-based vaccine effectiveness questions in future planning of both clinical trials as well as eventual program implementation.

Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study.

The COVID-19 pandemic has disrupted the seasonal patterns of several infectious diseases. Understanding when and where an outbreak may occur is vital for public health planning and response. We usually rely on well-functioning surveillance systems to monitor epidemic outbreaks. However, not all countries have a well-functioning surveillance system in place, or at least not for the pathogen in question. We utilized Google Trends search results for RSV-related keywords to identify outbreaks. We evaluated the strength of the Pearson correlation coefficient between clinical surveillance data and online search data and applied the Moving Epidemic Method (MEM) to identify country-specific epidemic thresholds. Additionally, we established pseudo-RSV surveillance systems, enabling internal stakeholders to obtain insights on the speed and risk of any emerging RSV outbreaks in countries with imprecise disease surveillance systems but with Google Trends data. Strong correlations between RSV clinical surveillance data and Google Trends search results from several countries were observed. In monitoring an upcoming RSV outbreak with MEM, data collected from both systems yielded similar estimates of country-specific epidemic thresholds, starting time, and duration. We demonstrate in this study the potential of monitoring disease outbreaks in real time and complement classical disease surveillance systems by leveraging online search data.

Flexible analytic model to inform multi-stakeholder pediatric vaccine scheduling decisions.

BACKGROUND: Pediatric immunization is important for preventing potentially life-threatening diseases in children. Over time, the number of recommended pediatric vaccines has increased and is likely to increase further as new vaccines are developed. Given the different number of doses for available vaccines and various constraints (e.g., the appropriate age for each dose of a vaccine or the time between doses), it is challenging to develop a recommended vaccination schedule or a catch-up schedule when a child falls behind on one or more vaccinations. METHODS: We developed an integer programming optimization model, enabled by Python programming and embedded into an Excel-based decision tool, to recommend childhood vaccination schedules or personalized catch-up schedules. The model recommends a vaccination schedule that balances the goal of being as close as possible to the clinically-indicated dosing schedules and the goal of minimizing clinic visits, and gives users the ability to trade off between these two goals. We illustrated the broad applicability of our proposed model with commonly-faced vaccine scheduling challenges in the United States. RESULTS: The illustrative computational case study confirms our model's ability to create personalized schedules based on each child's age and vaccination history, and to adjust appropriately when a new vaccine becomes available. CONCLUSIONS: The model presented in this paper fills the need for an easy-to-use tool to recommend vaccination schedules for de novo and catch-up purposes. It provides straightforward recommendations that can be easily used by physicians, is flexible to handle the requirements varying by region, and can be updated as new vaccines are approved for use.

Predicting and preventing COVID-19 outbreaks in indoor environments: an agent-based modeling study.

How to mitigate the spread of infectious diseases like COVID-19 in indoor environments remains an important research question. In this study, we propose an agent-based modeling framework to evaluate facility usage policies that aim to lower the probability of outbreaks. The proposed framework is individual-based, spatially-resolved with time resolution of up to 1 s, and takes into detailed account specific floor layouts, occupant schedules and movement. It enables decision makers to compute realistic contact networks and generate risk profiles of their facilities without relying on wearable devices, smartphone tagging or surveillance cameras. Our demonstrative modeling results indicate that not all facility occupants present the same risk of starting an outbreak, where the driver of outbreaks varies with facility layouts as well as individual occupant schedules. Therefore, generic mitigation strategies applied across all facilities should be considered inferior to tailored policies that take into account individual characteristics of the facilities of interest. The proposed modeling framework, implemented in Python and now available to the public in an open-source platform, enables such strategy evaluation.

Estimating the HIV Effective Reproduction Number in the United States and Evaluating HIV Elimination Strategies.

CONTEXT: The reproduction number is a fundamental epidemiologic concept used to assess the potential spread of infectious diseases and whether they can be eliminated. OBJECTIVE: We estimated the 2017 United States HIV effective reproduction number, Re, the average number of secondary infections from an infected person in a partially infected population. We analyzed the potential effects on Re of interventions aimed at improving patient flow rates along different stages of the HIV care continuum. We also examined these effects by individual transmission groups. DESIGN: We used the HIV Optimization and Prevention Economics (HOPE) model, a compartmental model of disease progression and transmission, and the next-generation matrix method to estimate Re. We then projected the impact of changes in HIV continuum-of-care interventions on the continuum-of-care flow rates and the estimated Re in 2020. SETTING: United States. PARTICIPANTS: The HOPE model simulated the sexually active US population and persons who inject drugs, aged 13 to 64 years, which was stratified into 195 subpopulations by transmission group, sex, race/ethnicity, age, male circumcision status, and HIV risk level. MAIN OUTCOME MEASURES: The estimated value of Re in 2017 and changes in Re in 2020 from interventions affecting the continuum-of-care flow rates. RESULTS: Our estimated HIV Re in 2017 was 0.92 [0.82, 0.94] (base case [min, max across calibration sets]). Among the interventions considered, the most effective way to reduce Re substantially below 1.0 in 2020 was to maintain viral suppression among those receiving HIV treatment. The greatest impact on Re resulted from changing the flow rates for men who have sex with men (MSM). CONCLUSIONS: Our results suggest that current prevention and treatment efforts may not be sufficient to move the country toward HIV elimination. Reducing Re to substantially below 1.0 may be achieved by an ongoing focus on early diagnosis, linkage to care, and sustained viral suppression especially for MSM.

Using video-analysis technology to estimate social mixing and simulate influenza transmission at a mass gathering.

Mass gatherings create settings conducive to infectious disease transmission. Empirical data to model infectious disease transmission at mass gatherings are limited. Video-analysis technology could be used to generate data on social mixing patterns needed for simulating influenza transmission at mass gatherings. We analyzed short video recordings of persons attending the GameFest event at a university in Troy, New York, in April 2013 to demonstrate the feasibility of this approach. Attendees were identified and tracked during three randomly selected time periods using an object-tracking algorithm. Tracks were analyzed to calculate the number and duration of unique pairwise contacts. A contact occurred each time two attendees were within 2 m of each other. We built and tested an agent-based stochastic influenza simulation model assuming two scenarios of mixing patterns in a geospatially accurate representation of the event venue -one calibrated to the mean cumulative contact duration estimated from GameFest video recordings and the other using a uniform mixing pattern. We compared one-hour attack rates (i.e., becoming infected) generated from these two scenarios following the introduction of a single infectious seed. Across the video recordings, 278 attendees were identified and tracked, resulting in 1,247 unique pairwise contacts with a cumulative mean contact duration of 74.76 s (SD: 80.71). The one-hour simulated mean attack rates were 2.17 % (95 % CI:1.45 - 2.82) and 0.21 % (95 % CI: 0.14 - 0.28) in the calibrated and uniform mixing model scenarios, respectively. We simulated influenza transmission at the GameFest event using social mixing data objectively captured through video-analysis technology. Microlevel geospatially accurate simulations can be used to assess the layout of event venues on social mixing and disease transmission. Future work can expand on this demonstration project to larger spatial and temporal scenes in more diverse settings.

Progression and transmission of HIV (PATH 4.0)-A new agent-based evolving network simulation for modeling HIV transmission clusters.

We present the Progression and Transmission of HIV (PATH 4.0), a simulation tool for analyses of cluster detection and intervention strategies. Molecular clusters are groups of HIV infections that are genetically similar, indicating rapid HIV transmission where HIV prevention resources are needed to improve health outcomes and prevent new infections. PATH 4.0 was constructed using a newly developed agent-based evolving network modeling (ABENM) technique and evolving contact network algorithm (ECNA) for generating scale-free networks. ABENM and ECNA were developed to facilitate simulation of transmission networks for low-prevalence diseases, such as HIV, which creates computational challenges for current network simulation techniques. Simulating transmission networks is essential for studying network dynamics, including clusters. We validated PATH 4.0 by comparing simulated projections of HIV diagnoses with estimates from the National HIV Surveillance System (NHSS) for 2010-2017. We also applied a cluster generation algorithm to PATH 4.0 to estimate cluster features, including the distribution of persons with diagnosed HIV infection by cluster status and size and the size distribution of clusters. Simulated features matched well with NHSS estimates, which used molecular methods to detect clusters among HIV nucleotide sequences of persons with HIV diagnosed during 2015-2017. Cluster detection and response is a component of the U.S. Ending the HIV Epidemic strategy. While surveillance is critical for detecting clusters, a model in conjunction with surveillance can allow us to refine cluster detection methods, understand factors associated with cluster growth, and assess interventions to inform effective response strategies. As surveillance data are only available for cases that are diagnosed and reported, a model is a critical tool to understand the true size of clusters and assess key questions, such as the relative contributions of clusters to onward transmissions. We believe PATH 4.0 is the first modeling tool available to assess cluster detection and response at the national-level and could help inform the national strategic plan.

Combinations of interventions to achieve a national HIV incidence reduction goal: insights from an agent-based model.

OBJECTIVE: Analyzing HIV care service targets for achieving a national goal of a 25% reduction in annual HIV incidence and evaluating the use of annual HIV diagnoses to measure progress in incidence reduction. DESIGN: Because there are considerable interactions among HIV care services, we model the dynamics of 'combinations' of increases in HIV care continuum targets to identify those that would achieve 25% reductions in annual incidence and diagnoses. METHODS: We used Progression and Transmission of HIV/AIDS 2.0, an agent-based dynamic stochastic simulation of HIV in the United States. RESULTS: A 25% reduction in annual incidence could be achieved by multiple alternative combinations of percentages of persons with diagnosed infection and persons with viral suppression including 85 and 68%, respectively, and 90 and 59%, respectively. The first combination corresponded to an 18% reduction in annual diagnoses, and infections being diagnosed at a median CD4 cell count of 372 cells/µl or approximately 3.8 years from time of infection. The corresponding values on the second combination are 4%, 462 cells/µl, and 2.0 years, respectively. CONCLUSION: Our analysis provides policy makers with specific targets and alternative choices to achieve the goal of a 25% reduction in HIV incidence. Reducing annual diagnoses does not equate to reducing annual incidence. Instead, progress toward reducing incidence can be measured by monitoring HIV surveillance data trends in CD4 cell count at diagnosis along with the proportion who have achieved viral suppression to determine where to focus local programmatic efforts.

Progression and Transmission of HIV/AIDS (PATH 2.0).

BACKGROUND: HIV transmission is the result of complex dynamics in the risk behaviors, partnership choices, disease stage and position along the HIV care continuum-individual characteristics that themselves can change over time. Capturing these dynamics and simulating transmissions to understand the chief sources of transmission remain important for prevention. METHODS: The Progression and Transmission of HIV/AIDS (PATH 2.0) is an agent-based model of a sample of 10,000 people living with HIV (PLWH), who represent all men who have sex with men (MSM) and heterosexuals living with HIV in the U.S.A. Persons uninfected were modeled as populations, stratified by risk and gender. The model included detailed individual-level data from several large national surveillance databases. The outcomes focused on average annual transmission rates from 2008 through 2011 by disease stage, HIV care continuum, and sexual risk group. RESULTS: The relative risk of transmission of those in the acute phase was nine-times [5th and 95th percentile simulation interval (SI): 7, 12] that of those in the non-acute phase, although, on average, those with acute infections comprised 1% of all PLWH. The relative risk of transmission was 24- to 50-times as high for those in the non-acute phase who had not achieved viral load suppression as compared with those who had. The relative risk of transmission among MSM was 3.2-times [SI: 2.7, 4.0] that of heterosexuals. Men who have sex with men and women generated 46% of sexually acquired transmissions among heterosexuals. CONCLUSIONS: The model results support a continued focus on early diagnosis, treatment and adherence to ART, with an emphasis on prevention efforts for MSM, a subgroup of whom appear to play a role in transmission to heterosexuals.