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Japanese Encephalitis (JE) is known for its high case fatality ratio (CFR) and long-term neurological sequelae. Over the years, efforts in JE treatment and control might change the JE fatality risk. However, previous estimates were from 10 years ago, using data from cases in the 10 years before this. Estimating JE disease severity is challenging because data come from countries with different JE surveillance systems, diagnostic methods, and study designs. Without precise and timely JE disease severity estimates, there is continued uncertainty about the JE disease burden and the effect of JE vaccination. We performed a systematic review to collate age-stratified JE fatality and morbidity data. We used a stepwise model selection with BIC as the selection criteria to identify JE CFR drivers. We used stacked regression, to predict country-specific JE CFR from 1961 to 2030. JE morbidity estimates were grouped from similar study designs to estimate the proportion of JE survivors with long-term neurological sequelae. We included 82 and 50 peer-reviewed journal articles published as of March 06 2021 for JE fatality and morbidity with 22 articles in both analyses. Results suggested overall JE CFR estimates of 26% (95% CI 22, 30) in 1961-1979, 20% (95% CI 17, 24) in 1980-1999, 14% (95% CI 11, 17) in 2000-2018, and 14% (95% CI 11, 17) in 2019-2030. Holding other variables constant, we found that JE fatality risk decreased over time (OR: 0.965; 95% CI: 0.947-0.983). Younger JE cases had a slightly higher JE fatality risk (OR: 1.012; 95% CI: 1.003-1.021). The odds of JE fatality in countries with JE vaccination is 0.802 (90% CI: 0.653-0.994; 95% CI: 0.62-1.033) times lower than the odds in countries without JE vaccination. Ten percentage increase in the percentage of rural population to the total population was associated with 15.35% (95% CI: 7.71, 22.57) decrease in JE fatality odds. Ten percentage increase in population growth rate is associated with 3.71% (90% CI: 0.23, 7.18; 95% CI: -0.4, 8.15) increase in JE fatality odds. Adjusting for the effect of year, rural population percent, age of JE cases, and population growth rate, we estimated that there was a higher odds of JE fatality in India compared to China. (OR: 5.46, 95% CI: 3.61-8.31). Using the prediction model we found that, in 2000-2018, Brunei, Pakistan, and Timor-Leste were predicted to have the highest JE CFR of 20%. Bangladesh, Guam, Pakistan, Philippines, and Vietnam had projected JE CFR over 20% for after 2018, whereas the projected JE CFRs were below 10% in China, Indonesia, Cambodia, Myanmar, Malaysia, and Thailand. For disability, we estimated that 36% (min-max 0-85) JE patients recovered fully at hospital discharge. One year after hospital discharge, 46% (min-max 0%-97%) JE survivors were estimated to live normally but 49% (min-max 3% - 86%)till had neurological sequelae. JE CFR estimates were lower than 20% after 2000. Our study provides an updated estimation of CFR and proportion of JE cases with long-term neurological sequelae that could help to refine cost-benefit assessment for JE control and elimination programs.

Despite estimates that, each year, as many as 300 million dengue virus (DENV) infections result in either no perceptible symptoms (asymptomatic) or symptoms that are sufficiently mild to go undetected by surveillance systems (inapparent), it has been assumed that these infections contribute little to onward transmission. However, recent blood-feeding experiments with Aedes aegypti mosquitoes showed that people with asymptomatic and pre-symptomatic DENV infections are capable of infecting mosquitoes. To place those findings into context, we used models of within-host viral dynamics and human demographic projections to (1) quantify the net infectiousness of individuals across the spectrum of DENV infection severity and (2) estimate the fraction of transmission attributable to people with different severities of disease. Our results indicate that net infectiousness of people with asymptomatic infections is 80% (median) that of people with apparent or inapparent symptomatic infections (95% credible interval (CI): 0-146%). Due to their numerical prominence in the infectious reservoir, clinically inapparent infections in total could account for 84% (CI: 82-86%) of DENV transmission. Of infections that ultimately result in any level of symptoms, we estimate that 24% (95% CI: 0-79%) of onward transmission results from mosquitoes biting individuals during the pre-symptomatic phase of their infection. Only 1% (95% CI: 0.8-1.1%) of DENV transmission is attributable to people with clinically detected infections after they have developed symptoms. These findings emphasize the need to (1) reorient current practices for outbreak response to adoption of pre-emptive strategies that account for contributions of undetected infections and (2) apply methodologies that account for undetected infections in surveillance programs, when assessing intervention impact, and when modeling mosquito-borne virus transmission.

Human mobility plays a critical role in the transmission dynamics of infectious diseases, influencing both their spread and the effectiveness of control measures. In the process of quantifying the real-time situation of an epidemic, the instantaneous reproduction number R t appears to be one of the useful metrics widely used by public health researchers, officials, and policy makers. Since individuals can contract infections both within their region of origin and in other regions they visit, ignoring human mobility in the estimation process overlooks its impact on transmission dynamics and can lead to biased estimates of R t , potentially misrepresenting the true epidemic situation. Our study explicitly integrates human mobility into a renewal-equation based disease transmission model to capture the mobility-driven effect on transmission. By incorporating pathogen-specific generation-time distribution, observational delay, the framework is epidemiologically informed and flexible to a wide range of diseases. We primarily validate the approach using simulated data, and demonstrate the limitations of estimating R t without considering mobility. We then apply it to two real-world mobility settings using SARS-CoV-2 mortality data: the regions of England and the LTLAs of North East region of England, and uncover the mobility driven effect on transmission at different spatial resolutions. This framework uses non-identifiable and widely accessible publicly available datasets, demonstrating its practical applicability and supporting better-informed and more targeted public health measures.

Dengue is an important vector-borne pathogen found across much of the world. Many factors complicate our understanding of the relationship between infection with one of the four dengue virus serotypes, and the observed incidence of disease. One of the factors is a large proportion of infections appear to result in no or few symptoms, while others result in severe infections. Estimates of the proportion of infections that result in no symptoms (inapparent) vary widely from 8% to 100%, depending on study and setting. To investigate the sources of variation of these estimates, we used a flexible framework to combine data from multiple cohort studies and cluster studies (follow-up around index cases). Building on previous observations that the immune status of individuals affects their probability of apparent disease, we estimated the probability of apparent disease among individuals with different exposure histories. In cohort studies mostly assessing infection in children, we estimated the proportion of infections that are apparent as 0.18 (95% Credible Interval, CI: 0.16, 0.20) for primary infections, 0.13 (95% CI: 0.05, 0.17) for individuals infected in the year following a first infection (cross-immune period), and 0.41 (95% CI: 0.36, 0.45) for those experiencing secondary infections after this first year. Estimates of the proportion of infections that are apparent from cluster studies were slightly higher than those from cohort studies for both primary and secondary infections, 0.22 (95% CI: 0.15, 0.29) and 0.57 (95% CI: 0.49, 0.68) respectively. We attempted to estimate the apparent proportion by serotype, but current published data were too limited to distinguish the presence or absence of serotype-specific differences. These estimates are critical for understanding dengue epidemiology. Most dengue data come from passive surveillance systems which not only miss most infections because they are asymptomatic and often underreported, but will also vary in sensitivity over time due to the interaction between previous incidence and the symptomatic proportion, as shown here. Nonetheless the underlying incidence of infection is critical to understanding susceptibility of the population and estimating the true burden of disease, key factors for effectively targeting interventions. The estimates shown here help clarify the link between past infection, observed disease, and current transmission intensity.

As the most widespread viral infection transmitted by the Aedes mosquitoes, dengue has been estimated to cause 51 million febrile disease cases globally each year. Although sustained vector control remains key to reducing the burden of dengue, current understanding of the key factors that explain the observed variation in the short- and long-term vector control effectiveness across different transmission settings remains limited. We used a detailed individual-based model to simulate dengue transmission with and without sustained vector control over a 30-year time frame, under different transmission scenarios. Vector control effectiveness was derived for different time windows within the 30-year intervention period. We then used the extreme gradient boosting algorithm to predict the effectiveness of vector control given the simulation parameters, and the resulting machine learning model was interpreted using Shapley Additive Explanations. According to our simulation outputs, dengue transmission would be nearly eliminated during the early stage of sustained and intensive vector control, but over time incidence would gradually bounce back to the pre-intervention level unless the intervention is implemented at a very high level of intensity. The time point at which intervention ceases to be effective is strongly influenced not only by the intensity of vector control, but also by the pre-intervention transmission intensity and the individual-level heterogeneity in biting risk. Moreover, the impact of many transmission model parameters on the intervention effectiveness is shown to be modified by the intensity of vector control, as well as to vary over time. Our study has identified some of the critical drivers for the difference in the time-varying effectiveness of sustained vector control across different dengue endemic settings, and the insights obtained will be useful to inform future model-based studies that seek to predict the impact of dengue vector control in their local contexts.

Abstract Background With more countries exiting lockdown, public health safety requires screening measures at international travel entry points that can prevent the reintroduction or importation of the severe acute respiratory syndrome-related coronavirus-2. Here, we estimate the number of cases captured, quarantining days averted and secondary cases expected to occur with screening interventions. Methods To estimate active case exportation risk from 153 countries with recorded coronavirus disease-2019 cases and deaths, we created a simple data-driven framework to calculate the number of infectious and upcoming infectious individuals out of 100 000 000 potential travellers from each country, and assessed six importation risk reduction strategies; Strategy 1 (S1) has no screening on entry, S2 tests all travellers and isolates test-positives where those who test negative at 7 days are permitted entry, S3 the equivalent but for a 14 day period, S4 quarantines all travellers for 7 days where all are subsequently permitted entry, S5 the equivalent for 14 days and S6 the testing of all travellers and prevention of entry for those who test positive. Results The average reduction in case importation across countries relative to S1 is 90.2% for S2, 91.7% for S3, 55.4% for S4, 91.2% for S5 and 77.2% for S6. An average of 79.6% of infected travellers are infectious upon arrival. For the top 100 exporting countries, an 88.2% average reduction in secondary cases is expected through S2 with the 7-day isolation of test-positives, increasing to 92.1% for S3 for 14-day isolation. A substantially smaller reduction of 30.0% is expected for 7-day all traveller quarantining, increasing to 84.3% for 14-day all traveller quarantining. Conclusions The testing and isolation of test-positives should be implemented provided good testing practices are in place. If testing is not feasible, quarantining for a minimum of 14 days is recommended with strict adherence measures in place.

Dengue is endemic in Vietnam with circulation of all four serotypes (DENV1-4) all year-round. It is hard to estimate the disease’s true serotype-specific transmission patterns from cases due to its high asymptomatic rate, low reporting rate and complex immunity and transmission dynamics. Seroprevalence studies have been used to great effect for understanding patterns of dengue transmission. We tested 991 population serum samples (ages 1–30 years, collected 2013 to 2017), 531 from Ho Chi Minh City and 460 from Khanh Hoa in Vietnam, using a flavivirus protein microarray assay. By applying our previously developed inference framework to the antibody profiles from this assay, we can (1) determine proportions of a population that have not been infected or infected, once, or more than once, and (2) infer the infecting serotype in those infected once. With these data, we then use mathematical models to estimate the force of infection (FOI) for all four DENV serotypes in HCMC and KH over 35 years up to 2017. Models with time-varying or serotype-specific DENV FOI assumptions fit the data better than constant FOI. Annual dengue FOI ranged from 0.005 (95%CI: 0.003–0.008) to 0.201 (95%CI: 0.174–0.228). FOI varied across serotypes, higher for DENV1 (95%CI: 0.033–0.048) and DENV2 (95%CI: 0.018–0.039) than DENV3 (95%CI: 0.007–0.010) and DENV4 (95%CI: 0.010–0.016). The use of the PMA on serial age-stratified cross-sectional samples increases the amount of information on transmission and population immunity, and should be considered for future dengue serological surveys, particularly to understand population immunity given vaccines with differential efficacy against serotypes, however, there remains limits to what can be inferred even using this assay.

Background Understanding the overall effectiveness of non-pharmaceutical interventions to control the COVID-19 pandemic and reduce the burden of disease is crucial for future pandemic planning. However, quantifying the effectiveness of specific control measures and the extent of missed infections, in the absence of early large-scale serological surveys or random community testing, has remained challenging. Methods Combining data on notified local COVID-19 cases with known and unknown sources of infections in Singapore with a branching process model, we reconstructed the incidence of missed infections during the early phase of the wild-type SARS-CoV-2 and Delta variant transmission. We then estimated the relative effectiveness of border control measures, case finding and contact tracing when there was no or low vaccine coverage in the population. We compared the risk of ICU admission and death between the wild-type SARS-CoV-2 and the Delta variant in notified cases and all infections. Results We estimated strict border control measures were associated with 0.2 (95% credible intervals, CrI 0.04–0.8) missed imported infections per notified case between July and December 2020, a decline from around 1 missed imported infection per notified case in the early phases of the pandemic. Contact tracing was estimated to identify 78% (95% CrI 62–93%) of the secondary infections generated by notified cases before the partial lockdown in Apr 2020, but this declined to 63% (95% CrI 56–71%) during the lockdown and rebounded to 78% (95% CrI 58–94%) during reopening in Jul 2020. The contribution of contact tracing towards overall outbreak control also hinges on ability to find cases with unknown sources of infection: 42% (95% CrI 12–84%) of such cases were found prior to the lockdown; 10% (95% CrI 7–15%) during the lockdown; 47% (95% CrI 17–85%) during reopening, due to increased testing capacity and health-seeking behaviour. We estimated around 63% (95% CrI 49–78%) of the wild-type SARS-CoV-2 infections were undetected during 2020 and around 70% (95% CrI 49–91%) for the Delta variant in 2021. Conclusions Combining models with case linkage data enables evaluation of the effectiveness of different components of outbreak control measures, and provides more reliable situational awareness when some cases are missed. Using such approaches for early identification of the weakest link in containment efforts could help policy makers to better redirect limited resources to strengthen outbreak control.

Background Dengue is a mosquito-borne viral infection which has been estimated to cause a global economic burden of US$8.9 billion per year. 40% of this estimate was due to what are known as productivity costs (the costs associated with productivity loss from both paid and unpaid work that results from illness, treatment or premature death). Although productivity costs account for a significant proportion of the estimated economic burden of dengue, the methods used to calculate them are often very variable within health economic studies. The aim of this review was to systematically examine the current estimates of the productivity costs associated with dengue episodes in Asia and to increase awareness surrounding how productivity costs are estimated. Method We searched PubMed and Web of Knowledge without date and language restrictions using terms related to dengue and cost and economics burden. The titles and abstracts of publications related to Asia were screened to identify relevant studies. The reported productivity losses and costs of non-fatal and fatal dengue episodes were then described and compared. Costs were adjusted for inflation to 2017 prices. Results We reviewed 33 relevant articles, of which 20 studies reported the productivity losses, and 31 studies reported productivity costs. The productivity costs varied between US$6.7–1445.9 and US$3.8–1332 for hospitalized and outpatient non-fatal episodes, respectively. The productivity cost associated with fatal dengue episodes varied between US$12,035-1,453,237. A large degree of this variation was due to the range of different countries being investigated and their corresponding economic status. However, estimates for a given country still showed notable variation. Conclusion We found that the estimated productivity costs associated with dengue episodes in Asia are notable. However, owing to the significant variation in methodology and approaches applied, the reported productivity costs of dengue episodes were often not directly comparable across studies. More consistent and transparent methodology regarding the estimation of productivity costs would help the estimates of the economic burden of dengue be more accurate and comparable across studies.

Background Aedes (Stegomyia) -borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes -borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. Methods We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.). Results We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002–2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures. Conclusions Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping.