COVID-19 control measures unexpectedly increased the duration of stay at High Speed Rail stations during the first community outbreak in Taiwan.

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.

Real-time forecasting of COVID-19 spread according to protective behavior and vaccination: autoregressive integrated moving average models.

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.

Mechanical transmission of dengue virus by Aedes aegypti may influence disease transmission dynamics during outbreaks.

BACKGROUND: Dengue virus outbreaks are increasing in number and severity worldwide. Viral transmission is assumed to require a minimum time period of viral replication within the mosquito midgut. It is unknown if alternative transmission periods not requiring replication are possible. METHODS: We used a mouse model of dengue virus transmission to investigate the potential of mechanical transmission of dengue virus. We investigated minimal viral titres necessary for development of symptoms in bitten mice and used resulting parameters to inform a new model of dengue virus transmission within a susceptible population. FINDINGS: Naïve mice bitten by mosquitoes immediately after they took partial blood meals from dengue infected mice showed symptoms of dengue virus, followed by mortality. Incorporation of mechanical transmission into mathematical models of dengue virus transmission suggest that this supplemental transmission route could result in larger outbreaks which peak sooner. INTERPRETATION: The potential of dengue transmission routes independent of midgut viral replication has implications for vector control strategies that target mosquito lifespan and suggest the possibility of similar mechanical transmission routes in other disease-carrying mosquitoes. FUNDING: This study was funded by grants from the National Health Research Institutes, Taiwan (04D2-MMMOST02), the Human Frontier Science Program (RGP0033/2021), the National Institutes of Health (1R01AI143698-01A1, R01AI151004 and DP2AI152071) and the Ministry of Science and Technology, Taiwan (MOST104-2321-B-400-016).

The evolution and structure of snake venom phosphodiesterase (svPDE) highlight its importance in venom actions.

For decades, studies of snake venoms focused on the venom-ome-specific toxins (VSTs). VSTs are dominant soluble proteins believed to contribute to the main venomous effects and emerged into gene clusters for fast adaptation and diversification of snake venoms. However, the conserved minor venom components, such as snake venom phosphodiesterase (svPDE), remain largely unexplored. Here, we focus on svPDE by genomic and transcriptomic analysis across snake clades and demonstrate that soluble svPDE is co-opted from the ancestral membrane-attached ENPP3 (ectonucleotide pyrophosphatase/phosphodiesterase 3) gene by replacing the original 5' exon with the exon encoding a signal peptide. Notably, the exons, promoters, and transcription/translation starts have been replaced multiple times during snake evolution, suggesting the evolutionary necessity of svPDE. The structural and biochemical analyses also show that svPDE shares the similar functions with ENPP family, suggesting its perturbation to the purinergic signaling and insulin transduction in venomous effects.

Discovering recent selection forces shaping the evolution of dengue viruses based on polymorphism data across geographic scales.

Incomplete selection makes it challenging to infer selection on genes at short time scales, especially for microorganisms, due to stronger linkage between loci. However, in many cases, the selective force changes with environment, time, or other factors, and it is of great interest to understand selective forces at this level to answer relevant biological questions. We developed a new method that uses the change in dN /dS , instead of the absolute value of dN /dS , to infer the dominating selective force based on sequence data across geographical scales. If a gene was under positive selection, dN /dS was expected to increase through time, whereas if a gene was under negative selection, dN /dS was expected to decrease through time. Assuming that the migration rate decreased and the divergence time between samples increased from between-continent, within-continent different-country, to within-country level, dN /dS of a gene dominated by positive selection was expected to increase with increasing geographical scales, and the opposite trend was expected in the case of negative selection. Motivated by the McDonald-Kreitman (MK) test, we developed a pairwise MK test to assess the statistical significance of detected trends in dN /dS . Application of the method to a global sample of dengue virus genomes identified multiple significant signatures of selection in both the structural and non-structural proteins. Because this method does not require allele frequency estimates and uses synonymous mutations for comparison, it is less prone to sampling error, providing a way to infer selection forces within species using publicly available genomic data from locations over broad geographical scales.

The impact of the COVID-19 preventive measures on influenza transmission: molecular and epidemiological evidence.

OBJECTIVE: We quantify the impact of COVID-19-related control measures on the spread of human influenza virus H1N1 and H3N2. METHODS: We analyzed case numbers to estimate the end of the 2019-2020 influenza season and compared it with the median of the previous 9 seasons. In addition, we used influenza molecular data to compare within-region and between-region genetic diversity and effective population size from 2019 to 2020. Finally, we analyzed personal behavior and policy stringency data for each region. RESULTS: The 2019-2020 influenza season ended earlier than the median of the previous 9 seasons in all regions. For H1N1 and H3N2, there was an increase in between-region genetic diversity in most pairs of regions between 2019 and 2020. There was a decrease in within-region genetic diversity for 12 of 14 regions for H1N1 and 9 of 12 regions for H3N2. There was a decrease in effective population size for 10 of 13 regions for H1N1 and 3 of 7 regions for H3N2. CONCLUSIONS: We found consistent evidence of a decrease in influenza incidence after the introduction of preventive measures due to COVID-19 emergence.

Distinguishing gene flow between malaria parasite populations.

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 (FST) 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.

Low parasite connectivity among three malaria hotspots in Thailand.

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.

Comparison of Estimated Effectiveness of Case-Based and Population-Based Interventions on COVID-19 Containment in Taiwan.

Importance: Taiwan is one of the few countries with initial success in COVID-19 control without strict lockdown or school closure. The reasons remain to be fully elucidated. Objective: To compare and evaluate the effectiveness of case-based (including contact tracing and quarantine) and population-based (including social distancing and facial masking) interventions for COVID-19 in Taiwan. Design, Setting, and Participants: This comparative effectiveness study used a stochastic branching process model using COVID-19 epidemic data from Taiwan, an island nation of 23.6 million people, with no locally acquired cases of COVID-19 reported for 253 days between April and December 2020. Main Outcomes and Measures: Effective reproduction number of COVID-19 cases (the number of secondary cases generated by 1 primary case) and the probability of outbreak extinction (0 new cases within 20 generations). For model development and calibration, an estimation of the incubation period (interval from exposure to symptom onset), serial interval (time between symptom onset in an infector-infectee pair), and the statistical distribution of the number of any subsequent infections generated by 1 primary case was calculated. Results: This study analyzed data from 158 confirmed COVID-19 cases (median age, 45 years; interquartile range, 25-55 years; 84 men [53%]). An estimated 55% (95% credible interval [CrI], 41%-68%) of transmission events occurred during the presymptomatic stage. In our estimated analysis, case detection, contact tracing, and 14-day quarantine of close contacts (regardless of symptoms) was estimated to decrease the reproduction number from the counterfactual value of 2.50 to 1.53 (95% CrI, 1.50-1.57), which would not be sufficient for epidemic control, which requires a value of less than 1. In our estimated analysis, voluntary population-based interventions, if used alone, were estimated to have reduced the reproduction number to 1.30 (95% CrI, 1.03-1.58). Combined case-based and population-based interventions were estimated to reduce the reproduction number to below unity (0.85; 95% CrI, 0.78-0.89). Results were similar for additional analyses with influenza data and sensitivity analyses. Conclusions and Relevance: In this comparative effectiveness research study, the combination of case-based and population-based interventions (with wide adherence) may explain the success of COVID-19 control in Taiwan in 2020. Either category of interventions alone would have been insufficient, even in a country with an effective public health system and comprehensive contact tracing program. Mitigating the COVID-19 pandemic requires the collaborative effort of public health professionals and the general public.

Transmission of SARS-CoV-2 before and after symptom onset: impact of nonpharmaceutical interventions in China.

Nonpharmaceutical interventions, such as contact tracing and quarantine, have been the primary means of controlling the spread of SARS-CoV-2; however, it remains uncertain which interventions are most effective at reducing transmission at the population level. Using serial interval data from before and after the rollout of nonpharmaceutical interventions in China, we estimate that the relative frequency of presymptomatic transmission increased from 34% before the rollout to 71% afterward. The shift toward earlier transmission indicates a disproportionate reduction in transmission post-symptom onset. We estimate that, following the rollout of nonpharmaceutical interventions, transmission post-symptom onset was reduced by 82% whereas presymptomatic transmission decreased by only 16%. The observation that only one-third of transmission was presymptomatic at baseline, combined with the finding that NPIs reduced presymptomatic transmission by less than 20%, suggests that the overall impact of NPIs was driven in large part by reductions in transmission following symptom onset. This implies that interventions which limit opportunities for transmission in the later stages of infection, such as contact tracing and isolation, are particularly important for control of SARS-CoV-2. Interventions which specifically reduce opportunities for presymptomatic transmission, such as quarantine of asymptomatic contacts, are likely to have smaller, but non-negligible, effects on overall transmission.

Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan.

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.

Evaluating the Performance of Malaria Genetics for Inferring Changes in Transmission Intensity Using Transmission Modeling.

Substantial progress has been made globally to control malaria, however there is a growing need for innovative new tools to ensure continued progress. One approach is to harness genetic sequencing and accompanying methodological approaches as have been used in the control of other infectious diseases. However, to utilize these methodologies for malaria, we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment, which all impact the level of genetic diversity and relatedness of malaria parasites. We develop an individual-based transmission model to simulate malaria parasite genetics parameterized using estimated relationships between complexity of infection and age from five regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterize the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The model predicted malaria prevalence with a mean absolute error of 0.055. Different assumptions about the availability of sample metadata were considered, with the most accurate predictions of malaria prevalence made when the clinical status and age of sampled individuals is known. Parasite genetics may provide a novel surveillance tool for estimating the prevalence of malaria in areas in which prevalence surveys are not feasible. However, the findings presented here reinforce the need for patient metadata to be recorded and made available within all future attempts to use parasite genetics for surveillance.

Face mask use in the general population and optimal resource allocation during the COVID-19 pandemic.

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.

Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan.

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.

Face mask use in the general population and optimal resource allocation during the COVID-19 pandemic.

The ongoing novel coronavirus disease (COVID-19) pandemic has rapidly spread in early 2020, causing tens of thousands of deaths, over a million cases and widespread socioeconomic disruption. With no vaccine available and numerous national healthcare systems reaching or exceeding capacity, interventions to limit transmission are urgently needed. While there is broad agreement that travel restrictions and closure of non-essential businesses and schools are beneficial in limiting local and regional spread, and such measures have been adopted in countries around the world, recommendations around the use of face masks for the general population are less consistent internationally. In this study, we examined the role of face masks in mitigating the spread of COVID-19 in the general population, using epidemic models to estimate the total reduction of infections and deaths under various scenarios. In particular, we examined the optimal deployment of face masks when resources are limited. We found that face masks, even with a limited protective effect, can reduce infections and deaths, and can delay the peak time of the epidemic. We consistently found that a random distribution of masks in the population was a suboptimal strategy when resources were limited. Prioritizing coverage among the elderly was more beneficial, while allocating a proportion of available resources for diagnosed infected cases provided further mitigation under a range of scenarios. In summary, face mask use, particularly for a pathogen with relatively common asymptomatic carriage, can effectively provide some mitigation of transmission, while balancing provision between vulnerable healthy persons and symptomatic persons can optimize mitigation efforts when resources are limited.

Mapping the travel patterns of people with malaria in Bangladesh.

BACKGROUND: Spread of malaria and antimalarial resistance through human movement present major threats to current goals to eliminate the disease. Bordering the Greater Mekong Subregion, southeast Bangladesh is a potentially important route of spread to India and beyond, but information on travel patterns in this area are lacking. METHODS: Using a standardised short survey tool, 2090 patients with malaria were interviewed at 57 study sites in 2015-2016 about their demographics and travel patterns in the preceding 2 months. RESULTS: Most travel was in the south of the study region between Cox's Bazar district (coastal region) to forested areas in Bandarban (31% by days and 45% by nights), forming a source-sink route. Less than 1% of travel reported was between the north and south forested areas of the study area. Farmers (21%) and students (19%) were the top two occupations recorded, with 67 and 47% reporting travel to the forest respectively. Males aged 25-49 years accounted for 43% of cases visiting forests but only 24% of the study population. Children did not travel. Women, forest dwellers and farmers did not travel beyond union boundaries. Military personnel travelled the furthest especially to remote forested areas. CONCLUSIONS: The approach demonstrated here provides a framework for identifying key traveller groups and their origins and destinations of travel in combination with knowledge of local epidemiology to inform malaria control and elimination efforts. Working with the NMEP, the findings were used to derive a set of policy recommendations to guide targeting of interventions for elimination.

Influenza A Hemagglutinin Passage Bias Sites and Host Specificity Mutations.

Animal studies aimed at understanding influenza virus mutations that change host specificity to adapt to replication in mammalian hosts are necessarily limited in sample numbers due to high cost and safety requirements. As a safe, higher-throughput alternative, we explore the possibility of using readily available passage bias data obtained mostly from seasonal H1 and H3 influenza strains that were differentially grown in mammalian (MDCK) and avian cells (eggs). Using a statistical approach over 80,000 influenza hemagglutinin sequences with passage information, we found that passage bias sites are most commonly found in three regions: (i) the globular head domain around the receptor binding site, (ii) the region that undergoes pH-dependent structural changes and (iii) the unstructured N-terminal region harbouring the signal peptide. Passage bias sites were consistent among different passage cell types as well as between influenza A subtypes. We also find epistatic interactions of site pairs supporting the notion of host-specific dependency of mutations on virus genomic background. The sites identified from our large-scale sequence analysis substantially overlap with known host adaptation sites in the WHO H5N1 genetic changes inventory suggesting information from passage bias can provide candidate sites for host specificity changes to aid in risk assessment for emerging strains.

Mapping imported malaria in Bangladesh using parasite genetic and human mobility data.

For countries aiming for malaria elimination, travel of infected individuals between endemic areas undermines local interventions. Quantifying parasite importation has therefore become a priority for national control programs. We analyzed epidemiological surveillance data, travel surveys, parasite genetic data, and anonymized mobile phone data to measure the spatial spread of malaria parasites in southeast Bangladesh. We developed a genetic mixing index to estimate the likelihood of samples being local or imported from parasite genetic data and inferred the direction and intensity of parasite flow between locations using an epidemiological model integrating the travel survey and mobile phone calling data. Our approach indicates that, contrary to dogma, frequent mixing occurs in low transmission regions in the southwest, and elimination will require interventions in addition to reducing imported infections from forested regions. Unlike risk maps generated from clinical case counts alone, therefore, our approach distinguishes areas of frequent importation as well as high transmission.

Correction to: Mapping malaria by combining parasite genomic and epidemiologic data.

The original article [1] contained an error in the presentation of Figure 1; this error has now been rectified and Figure 1 is now presented correctly.

Mapping malaria by combining parasite genomic and epidemiologic data.

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.