Monitoring the COVID-19 immune landscape in Japan

[Objectives] COVID-19 vaccination in Japan started on February 17, 2021. Because the timing of vaccination and the risk of severe COVID-19 greatly varied with age, the present study aimed to monitor the age-specific fractions of the population who were immune to SARS-CoV-2 infection after vaccination. [Methods] Natural infection remained extremely rare, accounting for less than 5% of the population by the end of 2021; thus, we ignored natural infection-induced immunity and focused on vaccine-induced immunity. We estimated the fraction of the population immune to infection by age group using vaccination registry data from February 17, 2021, to October 17, 2021. We accounted for two important sources of delay: (i) reporting delay and (ii) time from vaccination until immune protection develops. [Results] At the end of the observation period, the proportion of individuals still susceptible to SARS-CoV-2 infection substantially varied by age and was estimated to be ≥90% among people aged 0–14 years, in contrast to approximately 20% among the population aged ≥65 years. We also estimated the effective reproduction number over time using a next-generation matrix while accounting for differences in the proportion immune to infection by age. [Conclusion] The COVID-19 immune landscape greatly varied by age, and a substantial proportion of young adults remained susceptible. Vaccination contributed to a marked decrease in the reproduction number

Number of averted COVID-19 cases and deaths attributable to reduced risk in vaccinated individuals in Japan

Background: In Japan, vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was initiated on 17 February 2021, mainly using messenger RNA vaccines and prioritizing health care professionals. Whereas nationwide vaccination alleviated the coronavirus disease 2019 (COVID-19)-related burden, the population impact has yet to be quantified in Japan. We aimed to estimate the numbers of COVID-19 cases and deaths prevented that were attributable to the reduced risk among vaccinated individuals via a statistical modeling framework. Methods: We analyzed confirmed cases registered in the Health Center Real-time Information-sharing System on COVID-19 (3 March–30 November 2021) and publicly reported COVID-19-related deaths (24 March–30 November 2021). The vaccination coverage over this time course, classified by age and sex, was extracted from vaccine registration systems. The total numbers of prevented cases and deaths were calculated by multiplying the daily risk differences between unvaccinated and vaccinated individuals by the population size of vaccinated individuals. Findings: For both cases and deaths, the averted numbers were estimated to be the highest among individuals aged 65 years and older. In total, we estimated that 564, 596 (95% confidence interval: 477, 020–657, 525) COVID-19 cases and 18, 622 (95% confidence interval: 6522–33, 762) deaths associated with SARS-CoV-2 infection were prevented owing to vaccination during the analysis period (i.e., fifth epidemic wave, caused mainly by the Delta variant). Female individuals were more likely to be protected from infection following vaccination than male individuals whereas more deaths were prevented in male than in female individuals. Interpretation: The vaccination program in Japan led to substantial reductions in the numbers of COVID-19 cases and deaths (33% and 67%, respectively). The preventive effect will be further amplified during future pandemic waves caused by variants with shared antigenicity. Funding: This project was supported by the Japan Science and Technology Agency; the Japan Agency for Medical Research and Development; the Japan Society for the Promotion of Science; and the Ministry of Health, Labour and Welfare

Does anthropogenic introduction of guppy fish (Poecilia reticulata) impact faunal species diversity and abundance in natural aquatic habitats? A systematic review protocol

Abstract Background The guppy fish (Poecilia reticulata) is a tropical fish ancestrally linked to northern South America and the Caribbean. It is known to be very tolerant of and adaptable to new environments, and able to consume multiple food sources, including mosquito larvae. Consequently, guppies have been frequently introduced to non-native ecosystems to control mosquito populations, resulting in near-global distribution. Indeed, due to the increasing need for mosquito-borne disease control, guppy releases will likely continue, however there are concerns about potential adverse impacts on other species, biodiversity, and certain ecosystem functions. The most significant of these is local species extinction, and by extension, reduced biodiversity. Yet, the extent of these impacts has not been evaluated by scientific review. Accordingly, this study will examine and evaluate whether anthropogenic introduction of guppy fish (Poecilia reticulata) has impacts on faunal species diversity and abundance in natural aquatic habitats. The results of this review may have implications for environmental management and policy and inform ecosystem-based integrated vector management and public health policy. Methods Relevant scientific articles will be identified by searching electronic databases. Articles will be included if they report changes or differences, associated with guppy fish introduction, in at least one of these population parameters: (1) abundance of individuals in any species, (2) total abundance of individuals in all species present, (3) species richness, (4) species diversity, and (5) community evenness. Each article will be assessed by at least two independent reviewers against pre-defined inclusion/exclusion criteria. Snowballing reference lists of included articles will be conducted. At least two reviewers will critically appraise all included studies using the Collaboration for Environmental Evidence Critical Appraisal Tool (CEECAT) and any discrepancies will be resolved by discussion between the two or adjudication by a third author if agreement is not reached. Each study will also be subjected to data extraction against pre-defined qualitative and quantitative outcomes and results will be tabulated/presented in figures where appropriate. A meta-analysis will be carried out on outcome parameters with sufficient evidence. </jats:sec

Accelerating Progress Towards the 2030 Neglected Tropical Diseases Targets: How Can Quantitative Modeling Support Programmatic Decisions?

Over the past decade, considerable progress has been made in the control, elimination, and eradication of neglected tropical diseases (NTDs). Despite these advances, most NTD programs have recently experienced important setbacks; for example, NTD interventions were some of the most frequently and severely impacted by service disruptions due to the coronavirus disease 2019 (COVID-19) pandemic. Mathematical modeling can help inform selection of interventions to meet the targets set out in the NTD road map 2021-2030, and such studies should prioritize questions that are relevant for decision-makers, especially those designing, implementing, and evaluating national and subnational programs. In September 2022, the World Health Organization hosted a stakeholder meeting to identify such priority modeling questions across a range of NTDs and to consider how modeling could inform local decision making. Here, we summarize the outputs of the meeting, highlight common themes in the questions being asked, and discuss how quantitative modeling can support programmatic decisions that may accelerate progress towards the 2030 targets

Using model-based geostatistics for assessing the elimination of trachoma

Background: Trachoma is the commonest infectious cause of blindness worldwide. Efforts are being made to eliminate trachoma as a public health problem globally. However, as prevalence decreases, it becomes more challenging to precisely predict prevalence. We demonstrate how model-based geostatistics (MBG) can be used as a reliable, efficient, and widely applicable tool to assess the elimination status of trachoma. Methods: We analysed trachoma surveillance data from Brazil, Malawi, and Niger. We developed geostatistical Binomial models to predict trachomatous inflammation—follicular (TF) and trachomatous trichiasis (TT) prevalence. We proposed a general framework to incorporate age and gender in the geostatistical models, whilst accounting for residual spatial and non-spatial variation in prevalence through the use of random effects. We also used predictive probabilities generated by the geostatistical models to quantify the likelihood of having achieved the elimination target in each evaluation unit (EU). Results: TF and TT prevalence varied considerably by country, with Brazil showing the lowest prevalence and Niger the highest. Brazil and Malawi are highly likely to have met the elimination criteria for TF in each EU, but, for some EUs, there was high uncertainty in relation to the elimination of TT according to the model alone. In Niger, the predicted prevalence varied significantly across EUs, with the probability of having achieved the elimination target ranging from values close to 0% to 100%, for both TF and TT. Conclusions: We demonstrated the wide applicability of MBG for trachoma programmes, using data from different epidemiological settings. Unlike the standard trachoma prevalence survey approach, MBG provides a more statistically rigorous way of quantifying uncertainty around the achievement of elimination prevalence targets, through the use of spatial correlation. In addition to the analysis of existing survey data, MBG also provides an approach to identify areas in which more sampling effort is needed to improve EU classification. We advocate MBG as the new standard method for analysing trachoma survey outputs

Using model-based geostatistics for assessing the elimination of trachoma

Background: Trachoma is the commonest infectious cause of blindness worldwide. Efforts are being made to eliminate trachoma as a public health problem globally. However, as prevalence decreases, it becomes more challenging to precisely predict prevalence. We demonstrate how model-based geostatistics (MBG) can be used as a reliable, efficient, and widely applicable tool to assess the elimination status of trachoma. Methods: We analysed trachoma surveillance data from Brazil, Malawi, and Niger. We developed geostatistical Binomial models to predict trachomatous inflammation—follicular (TF) and trachomatous trichiasis (TT) prevalence. We proposed a general framework to incorporate age and gender in the geostatistical models, whilst accounting for residual spatial and non-spatial variation in prevalence through the use of random effects. We also used predictive probabilities generated by the geostatistical models to quantify the likelihood of having achieved the elimination target in each evaluation unit (EU). Results TF and TT prevalence varied considerably by country, with Brazil showing the lowest prevalence and Niger the highest. Brazil and Malawi are highly likely to have met the elimination criteria for TF in each EU, but, for some EUs, there was high uncertainty in relation to the elimination of TT according to the model alone. In Niger, the predicted prevalence varied significantly across EUs, with the probability of having achieved the elimination target ranging from values close to 0% to 100%, for both TF and TT. Conclusions: We demonstrated the wide applicability of MBG for trachoma programmes, using data from different epidemiological settings. Unlike the standard trachoma prevalence survey approach, MBG provides a more statistically rigorous way of quantifying uncertainty around the achievement of elimination prevalence targets, through the use of spatial correlation. In addition to the analysis of existing survey data, MBG also provides an approach to identify areas in which more sampling effort is needed to improve EU classification. We advocate MBG as the new standard method for analysing trachoma survey outputs

Accelerating Progress Towards the 2030 Neglected Tropical Diseases Targets: How Can Quantitative Modeling Support Programmatic Decisions?

Over the past decade, considerable progress has been made in the control, elimination, and eradication of neglected tropical diseases (NTDs). Despite these advances, most NTD programs have recently experienced important setbacks; for example, NTD interventions were some of the most frequently and severely impacted by service disruptions due to the coronavirus disease 2019 (COVID-19) pandemic. Mathematical modeling can help inform selection of interventions to meet the targets set out in the NTD road map 2021-2030, and such studies should prioritize questions that are relevant for decision-makers, especially those designing, implementing, and evaluating national and subnational programs. In September 2022, the World Health Organization hosted a stakeholder meeting to identify such priority modeling questions across a range of NTDs and to consider how modeling could inform local decision making. Here, we summarize the outputs of the meeting, highlight common themes in the questions being asked, and discuss how quantitative modeling can support programmatic decisions that may accelerate progress towards the 2030 targets