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.

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.

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.

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.

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.

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.

Chromosome position determines the success of double-strand break repair.

Repair of a chromosomal double-strand break (DSB) by gene conversion depends on the ability of the broken ends to encounter a donor sequence. To understand how chromosomal location of a target sequence affects DSB repair, we took advantage of genome-wide Hi-C analysis of yeast chromosomes to create a series of strains in which an induced site-specific DSB in budding yeast is repaired by a 2-kb donor sequence inserted at different locations. The efficiency of repair, measured by cell viability or competition between each donor and a reference site, showed a strong correlation (r = 0.85 and 0.79) with the contact frequencies of each donor with the DSB repair site. Repair efficiency depends on the distance between donor and recipient rather than any intrinsic limitation of a particular donor site. These results further demonstrate that the search for homology is the rate-limiting step in DSB repair and suggest that cells often fail to repair a DSB because they cannot locate a donor before other, apparently lethal, processes arise. The repair efficiency of a donor locus can be improved by four factors: slower 5' to 3' resection of the DSB ends, increased abundance of replication protein factor A (RPA), longer shared homology, or presence of a recombination enhancer element adjacent to a donor.

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.

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.

Sequence tag-based analysis of microbial population dynamics.

We describe sequence tag-based analysis of microbial populations (STAMP) for characterization of pathogen population dynamics during infection. STAMP analyzes the frequency changes of genetically 'barcoded' organisms to quantify population bottlenecks and infer the founding population size. Analyses of intraintestinal Vibrio cholerae revealed infection-stage and region-specific host barriers to infection and showed unexpected V. cholerae migration counter to intestinal flow. STAMP provides a robust, widely applicable analytical framework for high-confidence characterization of in vivo microbial dissemination.

THE REAL McCOIL: A method for the concurrent estimation of the complexity of infection and SNP allele frequency for malaria parasites.

As many malaria-endemic countries move towards elimination of Plasmodium falciparum, the most virulent human malaria parasite, effective tools for monitoring malaria epidemiology are urgent priorities. P. falciparum population genetic approaches offer promising tools for understanding transmission and spread of the disease, but a high prevalence of multi-clone or polygenomic infections can render estimation of even the most basic parameters, such as allele frequencies, challenging. A previous method, COIL, was developed to estimate complexity of infection (COI) from single nucleotide polymorphism (SNP) data, but relies on monogenomic infections to estimate allele frequencies or requires external allele frequency data which may not available. Estimates limited to monogenomic infections may not be representative, however, and when the average COI is high, they can be difficult or impossible to obtain. Therefore, we developed THE REAL McCOIL, Turning HEterozygous SNP data into Robust Estimates of ALelle frequency, via Markov chain Monte Carlo, and Complexity Of Infection using Likelihood, to incorporate polygenomic samples and simultaneously estimate allele frequency and COI. This approach was tested via simulations then applied to SNP data from cross-sectional surveys performed in three Ugandan sites with varying malaria transmission. We show that THE REAL McCOIL consistently outperforms COIL on simulated data, particularly when most infections are polygenomic. Using field data we show that, unlike with COIL, we can distinguish epidemiologically relevant differences in COI between and within these sites. Surprisingly, for example, we estimated high average COI in a peri-urban subregion with lower transmission intensity, suggesting that many of these cases were imported from surrounding regions with higher transmission intensity. THE REAL McCOIL therefore provides a robust tool for understanding the molecular epidemiology of malaria across transmission settings.

Modeling malaria genomics reveals transmission decline and rebound in Senegal.

To study the effects of malaria-control interventions on parasite population genomics, we examined a set of 1,007 samples of the malaria parasite Plasmodium falciparum collected in Thiès, Senegal between 2006 and 2013. The parasite samples were genotyped using a molecular barcode of 24 SNPs. About 35% of the samples grouped into subsets with identical barcodes, varying in size by year and sometimes persisting across years. The barcodes also formed networks of related groups. Analysis of 164 completely sequenced parasites revealed extensive sharing of genomic regions. In at least two cases we found first-generation recombinant offspring of parents whose genomes are similar or identical to genomes also present in the sample. An epidemiological model that tracks parasite genotypes can reproduce the observed pattern of barcode subsets. Quantification of likelihoods in the model strongly suggests a reduction of transmission from 2006-2010 with a significant rebound in 2012-2013. The reduced transmission and rebound were confirmed directly by incidence data from Thiès. These findings imply that intensive intervention to control malaria results in rapid and dramatic changes in parasite population genomics. The results also suggest that genomics combined with epidemiological modeling may afford prompt, continuous, and cost-effective tracking of progress toward malaria elimination.

Systematic analysis of protein identity between Zika virus and other arthropod-borne viruses.

OBJECTIVE: To analyse the proportions of protein identity between Zika virus and dengue, Japanese encephalitis, yellow fever, West Nile and chikungunya viruses as well as polymorphism between different Zika virus strains. METHODS: We used published protein sequences for the Zika virus and obtained protein sequences for the other viruses from the National Center for Biotechnology Information (NCBI) protein database or the NCBI virus variation resource. We used BLASTP to find regions of identity between viruses. We quantified the identity between the Zika virus and each of the other viruses, as well as within-Zika virus polymorphism for all amino acid k-mers across the proteome, with k ranging from 6 to 100. We assessed accessibility of protein fragments by calculating the solvent accessible surface area for the envelope and nonstructural-1 (NS1) proteins. FINDINGS: In total, we identified 294 Zika virus protein fragments with both low proportion of identity with other viruses and low levels of polymorphisms among Zika virus strains. The list includes protein fragments from all Zika virus proteins, except NS3. NS4A has the highest number (190 k-mers) of protein fragments on the list. CONCLUSION: We provide a candidate list of protein fragments that could be used when developing a sensitive and specific serological test to detect previous Zika virus infections.

Malaria life cycle intensifies both natural selection and random genetic drift.

Analysis of genome sequences of 159 isolates of Plasmodium falciparum from Senegal yields an extraordinarily high proportion (26.85%) of protein-coding genes with the ratio of nonsynonymous to synonymous polymorphism greater than one. This proportion is much greater than observed in other organisms. Also unusual is that the site-frequency spectra of synonymous and nonsynonymous polymorphisms are virtually indistinguishable. We hypothesized that the complicated life cycle of malaria parasites might lead to qualitatively different population genetics from that predicted from the classical Wright-Fisher (WF) model, which assumes a single random-mating population with a finite and constant population size in an organism with nonoverlapping generations. This paper summarizes simulation studies of random genetic drift and selection in malaria parasites that take into account their unusual life history. Our results show that random genetic drift in the malaria life cycle is more pronounced than under the WF model. Paradoxically, the efficiency of purifying selection in the malaria life cycle is also greater than under WF, and the relative efficiency of positive selection varies according to conditions. Additionally, the site-frequency spectrum under neutrality is also more skewed toward low-frequency alleles than expected with WF. These results highlight the importance of considering the malaria life cycle when applying existing population genetic tools based on the WF model. The same caveat applies to other species with similarly complex life cycles.

Clonal outbreak of Plasmodium falciparum infection in eastern Panama.

Identifying the source of resurgent parasites is paramount to a strategic, successful intervention for malaria elimination. Although the malaria incidence in Panama is low, a recent outbreak resulted in a 6-fold increase in reported cases. We hypothesized that parasites sampled from this epidemic might be related and exhibit a clonal population structure. We tested the genetic relatedness of parasites, using informative single-nucleotide polymorphisms and drug resistance loci. We found that parasites were clustered into 3 clonal subpopulations and were related to parasites from Colombia. Two clusters of Panamanian parasites shared identical drug resistance haplotypes, and all clusters shared a chloroquine-resistance genotype matching the pfcrt haplotype of Colombian origin. Our findings suggest these resurgent parasite populations are highly clonal and that the high clonality likely resulted from epidemic expansion of imported or vestigial cases. Malaria outbreak investigations that use genetic tools can illuminate potential sources of epidemic malaria and guide strategies to prevent further resurgence in areas where malaria has been eliminated.

Recurrent bottlenecks in the malaria life cycle obscure signals of positive selection.

Detecting signals of selection in the genome of malaria parasites is a key to identify targets for drug and vaccine development. Malaria parasites have a unique life cycle alternating between vector and host organism with a population bottleneck at each transition. These recurrent bottlenecks could influence the patterns of genetic diversity and the power of existing population genetic tools to identify sites under positive selection. We therefore simulated the site-frequency spectrum of a beneficial mutant allele through time under the malaria life cycle. We investigated the power of current population genetic methods to detect positive selection based on the site-frequency spectrum as well as temporal changes in allele frequency. We found that a within-host selective advantage is difficult to detect using these methods. Although a between-host transmission advantage could be detected, the power is decreased when compared with the classical Wright-Fisher (WF) population model. Using an adjusted null site-frequency spectrum that takes the malaria life cycle into account, the power of tests based on the site-frequency spectrum to detect positive selection is greatly improved. Our study demonstrates the importance of considering the life cycle in genetic analysis, especially in parasites with complex life cycles.

Sequence-based association and selection scans identify drug resistance loci in the Plasmodium falciparum malaria parasite.

Through rapid genetic adaptation and natural selection, the Plasmodium falciparum parasite--the deadliest of those that cause malaria--is able to develop resistance to antimalarial drugs, thwarting present efforts to control it. Genome-wide association studies (GWAS) provide a critical hypothesis-generating tool for understanding how this occurs. However, in P. falciparum, the limited amount of linkage disequilibrium hinders the power of traditional array-based GWAS. Here, we demonstrate the feasibility and power improvements gained by using whole-genome sequencing for association studies. We analyzed data from 45 Senegalese parasites and identified genetic changes associated with the parasites' in vitro response to 12 different antimalarials. To further increase statistical power, we adapted a common test for natural selection, XP-EHH (cross-population extended haplotype homozygosity), and used it to identify genomic regions associated with resistance to drugs. Using this sequence-based approach and the combination of association and selection-based tests, we detected several loci associated with drug resistance. These loci included the previously known signals at pfcrt, dhfr, and pfmdr1, as well as many genes not previously implicated in drug-resistance roles, including genes in the ubiquitination pathway. Based on the success of the analysis presented in this study, and on the demonstrated shortcomings of array-based approaches, we argue for a complete transition to sequence-based GWAS for small, low linkage-disequilibrium genomes like that of P. falciparum.

Genomic sequencing of Plasmodium falciparum malaria parasites from Senegal reveals the demographic history of the population.

Malaria is a deadly disease that causes nearly one million deaths each year. To develop methods to control and eradicate malaria, it is important to understand the genetic basis of Plasmodium falciparum adaptations to antimalarial treatments and the human immune system while taking into account its demographic history. To study the demographic history and identify genes under selection more efficiently, we sequenced the complete genomes of 25 culture-adapted P. falciparum isolates from three sites in Senegal. We show that there is no significant population structure among these Senegal sampling sites. By fitting demographic models to the synonymous allele-frequency spectrum, we also estimated a major 60-fold population expansion of this parasite population ∼20,000-40,000 years ago. Using inferred demographic history as a null model for coalescent simulation, we identified candidate genes under selection, including genes identified before, such as pfcrt and PfAMA1, as well as new candidate genes. Interestingly, we also found selection against G/C to A/T changes that offsets the large mutational bias toward A/T, and two unusual patterns: similar synonymous and nonsynonymous allele-frequency spectra, and 18% of genes having a nonsynonymous-to-synonymous polymorphism ratio >1.

A phantom study of the immobilization and the indications for using virtual isocenter in stereoscopic X-ray image guidance system referring to position localizer in frameless radiosurgery.

A frameless radiosurgery system is using a set of thermoplastic mask for fixation and stereoscopic X-ray imaging for alignment. The accuracy depends on mask fixation and imaging. Under certain circumstances, the guidance images may contain insufficient bony structures, resulting in lesser accuracy. A virtual isocenter function is designed for such scenarios. In this study, we investigated the immobilization and the indications for using virtual isocenter. Twenty-four arbitrary imaginary treatment targets (ITTs) in phantom were evaluated. The external Localizer with positioner films was used as reference. The alignments by using actual and virtual isocenter in image guidance were compared. The deviation of the alignment after mask removing and then resetting was also checked. The results illustrated that the mean deviation between the alignment by image guidance using actual isocenter (Iso(img)) and the localizer(Iso(loc)) was 2.26 mm ± 1.16 mm (standard deviation, SD), 1.66 mm ± 0.83 mm for using virtual isocenter. The deviation of the alignment by the image guidance using actual isocenter to the localizer before and after mask resetting was 7.02 mm ± 5.8 mm. The deviations before and after mask resetting were insignificant for the target center from skull edge larger than 80 mm on craniocaudal direction. The deviations between the alignment using actual and virtual isocenter in image guidance were not significant if the minimum distance from target center to skull edge was larger or equal to 30 mm. Due to an unacceptable deviation after mask resetting, the image guidance is necessary to improve the accuracy of frameless immobilization. A treatment isocenter less than 30 mm from the skull bone should be an indication for using virtual isocenter to align in image guidance. The virtual isocenter should be set as caudally as possible, and the sella of skull should be the ideal point.