Children's Health and Well-being during the COVID-19 Pandemic in Japan

The COVID-19 has caused challenges at all levels of society. It is necessary to, while carefully looking at impact that COVID-19 will have on children's health and well-being, and to steadily implement social work services accordingly. This paper highlights some key challenges and concerns for health and well-being on children and adolescents in Japan during COVID-19 pandemic. The purpose of this paper is to consider how the COVID-19 pandemic and the policy taken to mitigating the risk of COVID-19 have impacted children in Japan. In conclusion, we are not saying that COVID-19 policy responses such as school closures overall are ineffective for mitigating the COVID-19 pandemic in Japan. However, as we have seen, school closing policy is likely to have a negative impact on children’s health and well-being such as increased risks of mental health, abuse and suicide. The important point is that these impacts is not the impact of COVID-19 but the impact of the policy responses to COVID-19. The policy responses are likely to lead to a range of unexpected impacts and results. Therefore, policy makers, social workers and other professionals always should consider for the impact of policy responses to COVID-19 on children and adolescents.

Stringent Government Policies Are Associated With a Lower COVID-19 Spread Rate

The outbreak of COVID-19 has prompted a wide range of policy responses from governments around the world. In this study, we investigate the effect of governmental policies on the spread of the COVID-19 in a cross-country setting using the Oxford COVID-19 Government Response Stringency Index. We find that stringent government policies overall, and the following policies in particular, are associated with a lower spread rate of COVID-19 cases: workplace closing, restrictions on gatherings, close of public transport, stay-at-home order, restrictions on internal movement, and international travel controls; while school closing and public events cancellation are not associated with a lower COVID-19 spread. After including all policies into one single regression and examining their associations simultaneously with the virus spread, we find that the two policies stand out and remain to have a negative association with the COVID-19 spread: close of public transport and restrictions on international travel. Finally, we show that when countries are more oriented toward a tight culture, their governmental strict policies effect on the spread of COVID-19 becomes 1.5 – 3 times stronger than countries more toward a loose culture. Our findings suggest that the governments need to carefully implement policies to cope with the COVID-19 spread in their own social and cultural context.

Countries with delayed COVID-19 introduction – characteristics, drivers, gaps, and opportunities

Background Three months after the first reported cases, COVID-19 had spread to nearly 90% of World Health Organization (WHO) member states and only 24 countries had not reported cases as of 30 March 2020. This analysis aimed to 1) assess characteristics, capability to detect and monitor COVID-19, and disease control measures in these 24 countries, 2) understand potential factors for the reported delayed COVID-19 introduction, and 3) identify gaps and opportunities for outbreak preparedness, particularly in low and middle-income countries (LMICs). We collected and analyzed publicly available information on country characteristics, COVID-19 testing, influenza surveillance, border measures, and preparedness activities in these countries. We also assessed the association between the temporal spread of COVID-19 in all countries with reported cases with globalization indicator and geographic location. Results Temporal spreading of COVID-19 was strongly associated with countries’ globalization indicator and geographic location. Most of the 24 countries with delayed COVID-19 introduction were LMICs; 88% were small island or landlocked developing countries. As of 30 March 2020, only 38% of these countries reported in-country COVID-19 testing capability, and 71% reported conducting influenza surveillance during the past year. All had implemented two or more border measures, (e.g., travel restrictions and border closures) and multiple preparedness activities (e.g., national preparedness plans and school closing). Conclusions Limited testing capacity suggests that most of the 24 delayed countries may have lacked the capability to detect and identify cases early through sentinel and case-based surveillance. Low global connectedness, geographic isolation, and border measures were common among these countries and may have contributed to the delayed introduction of COVID-19 into these countries. This paper contributes to identifying opportunities for pandemic preparedness, such as increasing disease detection, surveillance, and international collaborations. As the global situation continues to evolve, it is essential for countries to improve and prioritize their capacities to rapidly prevent, detect, and respond, not only for COVID-19, but also for future outbreaks.

COVID-19-related school closing aggravate obesity and glucose intolerance in pediatric patients with obesity

It is important to pay attention to the indirect effects of the social distancing implemented to prevent the spread of coronavirus disease 2019 (COVID-19) pandemic on children and adolescent health. The aim of the present study was to explore impacts of a reduction in physical activity caused by COVID-19 outbreak in pediatric patients diagnosed with obesity. This study conducted between pre-school closing and school closing period and 90 patients aged between 6- and 18-year-old were included. Comparing the variables between pre-school closing period and school closing period in patients suffering from obesity revealed significant differences in variables related to metabolism such as body weight z-score, body mass index z-score, liver enzymes and lipid profile. We further evaluated the metabolic factors related to obesity. When comparing patients with or without nonalcoholic fatty liver disease (NAFLD), only hemoglobin A1c (HbA1c) was the only difference between the two time points (p < 0.05). We found that reduced physical activity due to school closing during COVID-19 pandemic exacerbated obesity among children and adolescents and negatively affects the HbA1C increase in NAFLD patients compared to non-NAFLD patients.

Variational-LSTM autoencoder to forecast the spread of coronavirus across the globe

Modelling the spread of coronavirus globally while learning trends at global and country levels remains crucial for tackling the pandemic. We introduce a novel variational-LSTM Autoencoder model to predict the spread of coronavirus for each country across the globe. This deep Spatio-temporal model does not only rely on historical data of the virus spread but also includes factors related to urban characteristics represented in locational and demographic data (such as population density, urban population, and fertility rate), an index that represents the governmental measures and response amid toward mitigating the outbreak (includes 13 measures such as: 1) school closing, 2) workplace closing, 3) cancelling public events, 4) close public transport, 5) public information campaigns, 6) restrictions on internal movements, 7) international travel controls, 8) fiscal measures, 9) monetary measures, 10) emergency investment in health care, 11) investment in vaccines, 12) virus testing framework, and 13) contact tracing). In addition, the introduced method learns to generate a graph to adjust the spatial dependences among different countries while forecasting the spread. We trained two models for short and long-term forecasts. The first one is trained to output one step in future with three previous timestamps of all features across the globe, whereas the second model is trained to output 10 steps in future. Overall, the trained models show high validation for forecasting the spread for each country for short and long-term forecasts, which makes the introduce method a useful tool to assist decision and policymaking for the different corners of the globe.

A model to rate strategies for managing disease due to COVID-19 infection

Considering looming fatality and economic recession, effective policy making based on ongoing COVID-19 pandemic is an urgent and standing issue. Numerous issues for controlling infection have arisen from public discussion led by medical professionals. Yet understanding of these factors has been necessarily qualitative and control measures to correct unfavorable trends specific to an infection area have been lacking. The logical implement for control is a large scale stochastic model with countless parameters lacking robustness and requiring enormous data. This paper presents a remedy for this vexing problem by proposing an alternative approach. Machine learning has come to play a widely circulated role in the study of complex data in recent times. We demonstrate that when machine learning is employed together with the mechanistic framework of a mathematical model, there can be a considerably enhanced understanding of complex systems. A mathematical model describing the viral infection dynamics reveals two transmissibility parameters influenced by the management strategies in the area for the control of the current pandemic. Both parameters readily yield the peak infection rate and means for flattening the curve, which is correlated to different management strategies by employing machine learning, enabling comparison of different strategies and suggesting timely alterations. Treatment of population data with the model shows that restricted non-essential business closure, school closing and strictures on mass gathering influence the spread of infection. While a rational strategy for initiation of an economic reboot would call for a wider perspective of the local economics, the model can speculate on its timing based on the status of the infection as reflected by its potential for an unacceptably renewed viral onslaught.

The relative effects of non-pharmaceutical interventions on early Covid-19 mortality: natural experiment in 130 countries

BackgroundConcurrent non-pharmaceutical interventions have been implemented around the world to control Covid-19 transmission. Their general effect on reducing virus transmission is proven, but they can also be negative to mental health and economies, and transmission behaviours can also change in absence of mandated policies. Their relative impact on Covid-19 attributed mortality rates, enabling policy selection for maximal benefit with minimal disruption, is not well established.MethodsWe exploited variations in nine non-pharmaceutical interventions implemented in 130 countries (3250 observations) in two periods chosen to limit reverse causality: i) prior to first Covid-19 death (when policymakers could not possibly be reacting to deaths in their own country); and, ii) 14-days-post first Covid-19 death (when deaths were still low, on average). We examined associations with daily deaths per million in each subsequent 24-day period (the time between virus transmission and mortality) which could only be affected by the policy period. A mean score of strictness and timeliness was coded for each intervention. Days in each country were indexed in time by first reported Covid-19 death to proxy for virus transmission rate. Multivariable linear regression models of Covid-19 mortality rates on all concurrent interventions were adjusted for seasonality, potential confounders, and potential cross-country differences in their mortality definitions. Robustness was checked by removing countries with known data reporting issues and with non-linear, negative binomial, models.ResultsAfter adjusting for multiple concurrent interventions and confounders, and accounting for both timing and strictness of interventions, earlier and stricter school (−1.23 daily deaths per million, 95% CI -2.20 -0.27) and workplace closures (−0.26, 95% CI -0.46 -0.05) were associated with lower Covid-19 mortality rates. Only controlling for strictness international travel controls, and only controlling for timing later restrictions on gatherings, were also associated with lower Covid-19 mortality. Other interventions, such as stay-at-home orders or restrictions on public transport, were not significantly associated with differences in mortality rates across countries. Findings were robust across multiple statistical approaches.ConclusionsFocusing on ‘compulsory’, particularly school closing, not ‘voluntary’ reduction of social interactions with mandated policies appears to have been the most effective strategy to mitigate early Covid-19 mortality.

Estimating the impact of mobility patterns on COVID-19 infection rates in 11 European countries

Background As governments across Europe have issued non-pharmaceutical interventions (NPIs) such as social distancing and school closing, the mobility patterns in these countries have changed. Most states have implemented similar NPIs at similar time points. However, it is likely different countries and populations respond differently to the NPIs and that these differences cause mobility patterns and thereby the epidemic development to change. Methods We build a Bayesian model that estimates the number of deaths on a given day dependent on changes in the basic reproductive number, R0, due to differences in mobility patterns. We utilise mobility data from Google mobility reports using five different categories: retail and recreation, grocery and pharmacy, transit stations, workplace and residential. The importance of each mobility category for predicting changes in R0 is estimated through the model. Findings The changes in mobility have a considerable overlap with the introduction of governmental NPIs, highlighting the importance of government action for population behavioural change. The shift in mobility in all categories shows high correlations with the death rates 1 month later. Reduction of movement within the grocery and pharmacy sector is estimated to account for most of the decrease in R0. Interpretation Our model predicts 3-week epidemic forecasts, using real-time observations of changes in mobility patterns, which can provide governments with direct feedback on the effects of their NPIs. The model predicts the changes in a majority of the countries accurately but overestimates the impact of NPIs in Sweden and Denmark and underestimates them in France and Belgium. We also note that the exponential nature of all epidemiological models based on the basic reproductive number, R0 cause small errors to have extensive effects on the predicted outcome.