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The ongoing novel coronavirus disease (COVID-19) pandemic has already infected millions worldwide and, with no vaccine available, interventions to mitigate transmission are urgently needed. While there is broad agreement that travel restrictions and social distancing are beneficial in limiting spread, recommendations around face mask use are inconsistent. Here, we use mathematical modeling to examine the epidemiological impact of face masks, considering resource limitations and a range of supply and demand dynamics. Even with a limited protective effect, face masks can reduce total infections and deaths, and can delay the peak time of the epidemic. However, random distribution of masks is generally suboptimal; prioritized coverage of the elderly improves outcomes, while retaining resources for detected cases provides further mitigation under a range of scenarios. Face mask use, particularly for a pathogen with relatively common asymptomatic carriage, is an effective intervention strategy, while optimized distribution is important when resources are limited. Recommendations regarding the use of face masks as a preventive measure for COVID-19 are inconsistent. Here, the authors show that optimal distribution of surgical-standard face masks in the population, or universal coverage of homemade face coverings, could reduce total infections and deaths.

Background As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. Methods In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. Results We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. Conclusions To prepare for the potential spread within Taiwan, we utilized Facebook’s aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.

Background Recent global progress in scaling up malaria control interventions has revived the goal of complete elimination in many countries. Decreasing transmission intensity generally leads to increasingly patchy spatial patterns of malaria transmission in elimination settings, with control programs having to accurately identify remaining foci in order to efficiently target interventions. Findings The role of connectivity between different pockets of local transmission is of increasing importance as programs near elimination since humans are able to transfer parasites beyond the limits of mosquito dispersal, thus re-introducing parasites to previously malaria-free regions. Here, we discuss recent advances in the quantification of spatial epidemiology of malaria, particularly Plasmodium falciparum , in the context of transmission reduction interventions. Further, we highlight the challenges and promising directions for the development of integrated mapping, modeling, and genomic approaches that leverage disparate datasets to measure both connectivity and transmission. Conclusion A more comprehensive understanding of the spatial transmission of malaria can be gained using a combination of parasite genetics and epidemiological modeling and mapping. However, additional molecular and quantitative methods are necessary to answer these public health-related questions.

During the COVID-19 pandemic, Taiwan has implemented strict border controls and community spread prevention measures. As part of these efforts, the government also implemented measures for public transportation. In Taiwan, there are two primary public transportation systems: Taiwan Railways (TR) is commonly utilized for local travel, while the Taiwan High-Speed Rail (THSR) is preferred for business trips and long-distance journeys due to its higher speed. In this study, we examined the impact of these disease prevention measures on the number of passengers and duration of stay in two major public transportation systems during the first community outbreak from April 29th to May 29th, 2021. Using data from a local telecommunications company, our study observed an expected decrease in the number of passengers after the cancellation of non-reserved seats at both TR and THSR stations across all 19 cities in the main island of Taiwan. Surprisingly, however, the duration of stay in some of the cities unexpectedly increased, especially at THSR stations. This unanticipated rise in the duration of stay has the potential to elevate contact probability among passengers and, consequently, the transmission rate. Our analysis shows that intervention policies may result in unforeseen outcomes, highlighting the crucial role of human mobility data as a real-time reference for policymakers. It enables them to monitor the impact of disease prevention measures and facilitates informed, data-driven decision-making.

Background Mathematical and statistical models are used to predict trends in epidemic spread and determine the effectiveness of control measures. Automatic regressive integrated moving average (ARIMA) models are used for time-series forecasting, but only few models of the 2019 coronavirus disease (COVID-19) pandemic have incorporated protective behaviors or vaccination, known to be effective for pandemic control. Methods To improve the accuracy of prediction, we applied newly developed ARIMA models with predictors (mask wearing, avoiding going out, and vaccination) to forecast weekly COVID-19 case growth rates in Canada, France, Italy, and Israel between January 2021 and March 2022. The open-source data was sourced from the YouGov survey and Our World in Data. Prediction performance was evaluated using the root mean square error (RMSE) and the corrected Akaike information criterion (AICc). Results A model with mask wearing and vaccination variables performed best for the pandemic period in which the Alpha and Delta viral variants were predominant (before November 2021). A model using only past case growth rates as autoregressive predictors performed best for the Omicron period (after December 2021). The models suggested that protective behaviors and vaccination are associated with the reduction of COVID-19 case growth rates, with booster vaccine coverage playing a particularly vital role during the Omicron period. For example, each unit increase in mask wearing and avoiding going out significantly reduced the case growth rate during the Alpha/Delta period in Canada (–0.81 and –0.54, respectively; both p < 0.05). In the Omicron period, each unit increase in the number of booster doses resulted in a significant reduction of the case growth rate in Canada (–0.03), Israel (–0.12), Italy (–0.02), and France (–0.03); all p < 0.05. Conclusions The key findings of this study are incorporating behavior and vaccination as predictors led to accurate predictions and highlighted their significant role in controlling the pandemic. These models are easily interpretable and can be embedded in a “real-time” schedule with weekly data updates. They can support timely decision making about policies to control dynamically changing epidemics.

While there has been significant progress in controlling falciparum malaria in the Lao People’s Democratic Republic (PDR), sporadic cases persist in southern provinces where the extent and patterns of transmission remain largely unknown. To assess parasite transmission in this area, 53 Plasmodium falciparum (Pf) positive cases detected through active test and treat campaigns from December 2017 to November 2018 were sequenced, targeting 204 highly polymorphic amplicons. Two R packages, MOIRE and Dcifer , were applied to assess the multiplicity of infections (MOI), effective MOI (eMOI), within-host parasite relatedness, and between-host parasite relatedness ( r b ^ ). Genomic data were integrated with survey data to characterize the temporal and spatial structures of identified clusters. The positive cases were mainly captured during the focal test and treat campaign conducted in 2018, and in the Pathoomphone area, which had the highest test positivity and forest activity. About 30% of the cases were polyclonal infections, with over half of theses (63%) showing within-host relatedness greater than 0.6, suggesting that cotransmission rather than superinfection was primarily responsible for maintaining polyclonality. A large majority of cases (81%) were infected by parasites genetically linked to one or more other cases. We identified five genetically distinct clusters in forest fringe villages within the Pathoomphone district, characterized by a high degree of genetic relatedness between parasites (mean r b ^ = 0.8). Four smaller clusters of 2–3 cases linked Moonlapamok and Pathoomphone districts, with an average r b ^ of 0.6, suggesting cross-district transmission. Most of the clustered cases occurred within 20 km and 2 months of each other, consistent with focal transmission. Transmission clusters identified in this study confirm the role of ongoing focal parasite transmission occurring within the forest or forest-fringe in the highly mobile population.

Identifying sources and sinks of malaria transmission is critical for designing effective intervention strategies particularly as countries approach elimination. The number of malaria cases in Thailand decreased 90% between 2012 and 2020, yet elimination has remained a major public health challenge with persistent transmission foci and ongoing importation. There are three main hotspots of malaria transmission in Thailand: Ubon Ratchathani and Sisaket in the Northeast; Tak in the West; and Yala in the South. However, the degree to which these hotspots are connected via travel and importation has not been well characterized. Here, we develop a metapopulation model parameterized by mobile phone call detail record data to estimate parasite flow among these regions. We show that parasite connectivity among these regions was limited, and that each of these provinces independently drove the malaria transmission in nearby provinces. Overall, our results suggest that due to the low probability of domestic importation between the transmission hotspots, control and elimination strategies can be considered separately for each region.

Background The COVID-19 pandemic prompted widespread preventive measures and altered individual contact behaviors. However, limited research has examined how health beliefs shape these behavioral changes. Methods This study investigates the association between health beliefs and close contact patterns during two COVID-19 waves in Taiwan, using a longitudinal survey conducted between July 2020 and June 2021 (the 2021 summer wave, n  = 728) and a cross-sectional survey conducted between September and November 2022 (the 2022 autumn wave, n  = 2,793). Participants reported close contacts from the previous day, categorized as household, non-household necessary, and non-household unnecessary contacts. Generalized estimating equations and zero-inflated Poisson models were applied to assess associations. Results The results revealed that perceived benefit from COVID-19 preventive measures and perceived severity of infection remained high since the onset of the 2021 summer wave in Taiwan. The number of non-household contacts declined during peak periods and was associated with health beliefs in distinct ways. High perceived barriers to COVID-19 preventive measures and high perceived susceptibility to infection were linked to a greater number of non-household contacts, whereas high perceived benefit from preventive measures correlated with fewer non-household contacts. Additionally, our longitudinal analysis demonstrated that health beliefs are not static but evolve over time in response to changing epidemic conditions and public awareness. Conclusion These findings underscore the dynamic role of health risk perception in shaping contact behavior and highlight the value of distinguishing contact types to guide public health policies and modeling of disease transmission.

Measuring gene flow between malaria parasite populations in different geographic locations can provide strategic information for malaria control interventions. Multiple important questions pertaining to the design of such studies remain unanswered, limiting efforts to operationalize genomic surveillance tools for routine public health use. This report examines the use of population-level summaries of genetic divergence ( F ST ) and relatedness (identity-by-descent) to distinguish levels of gene flow between malaria populations, focused on field-relevant questions about data size, sampling, and interpretability of observations from genomic surveillance studies. To do this, we use P. falciparum whole genome sequence data and simulated sequence data approximating malaria populations evolving under different current and historical epidemiological conditions. We employ mobile-phone associated mobility data to estimate parasite migration rates over different spatial scales and use this to inform our analysis. This analysis underscores the complementary nature of divergence- and relatedness-based metrics for distinguishing gene flow over different temporal and spatial scales and characterizes the data requirements for using these metrics in different contexts. Our results have implications for the design and implementation of malaria genomic surveillance studies.

Little is known about environmental transmission of Mycobacterium kansasii. We retrospectively investigated potential environmental acquisition, primarily water sources, of M. kansasii among 216 patients with pulmonary disease from an industrial city in Taiwan during 2015-2017. We analyzed sputum mycobacterial cultures using whole-genome sequencing and used hierarchical Bayesian spatial network methods to evaluate risk factors for genetic relatedness of M. kansasii strains. The mean age of participants was 67 years; 24.1% had previously had tuberculosis. We found that persons from districts served by 2 water purification plants were at higher risk of being infected with genetically related M. kansasii isolates. The adjusted odds ratios were 1.81 (1.25-2.60) for the Weng Park plant and 1.39 (1.12-1.71) for the Fongshan plant. Those findings unveiled the association between water purification plants and M. kansasii pulmonary disease, highlighting the need for further environmental investigations to evaluate the risk for M. kansasii transmission.