We model the growth of scientific literature related to COVID-19 and forecast the expected growth from 1 June 2021. Considering the significant scientific and financial efforts made by the research community to find solutions to end the COVID-19 pandemic, an unprecedented volume of scientific outputs is being produced. This questions the capacity of scientists, politicians and citizens to maintain infrastructure, digest content and take scientifically informed decisions. A crucial aspect is to make predictions to prepare for such a large corpus of scientific literature. Here we base our predictions on the Autoregressive Integrated Moving Average (ARIMA) and exponential smoothing models using the Dimensions database. This source has the particularity of including in the metadata information on the date in which papers were indexed. We present global predictions, plus predictions in three specific settings: by type of access (Open Access), by domain-specific repository (SSRN and MedRxiv) and by several research fields. We conclude by discussing our findings.

Foodborne diseases are important causes of illness and death. The first estimates of their burden were published by WHO in 2015. We updated WHO estimates of the global, regional, subregional, and national foodborne disease burden caused by 42 infectious and chemical hazards in 2021, including time trends for 2000-21. We provide a high-level summary of foodborne disease burden, expressed as incidence, deaths, and disability-adjusted life-years (DALYs). Data for burden estimation were provided from a WHO-commissioned series of systematic reviews on the incidence, aetiology, sequelae, and case fatality or mortality of the hazards. Data were analysed using hierarchical meta-regression modelling with geographical clustering and a global linear time trend, disease-specific computational models, and uncertainty propagation through Monte Carlo simulations to calculate 95% uncertainty intervals. Attribution to foodborne transmission was principally based on a structured expert judgement process. Economic impact was measured as lost productivity. For 2021, foodborne transmission of the 42 hazards caused 866 million (95% uncertainty interval 680-1090) illnesses, 1·52 million (0·783-2·51) deaths, and 57·1 million (39·4-81·1) DALYs. Inorganic arsenic, lead, and non-typhoidal Salmonella enterica (diarrhoeal and invasive disease) resulted in the most DALYs. The greatest burden of foodborne disease was in the African and South-East Asia regions. The incidence in children younger than 5 years was 2·7 times higher than in people aged 5 years or older, resulting in 4·3 times the rate of DALYs. The total burden from all hazards decreased over time. In 2021, these 42 hazards resulted in productivity losses of US$310 billion in nominal terms, and US$647 billion after adjusting for purchasing power parity. Foodborne diseases causes a burden similar to that from tuberculosis, HIV and AIDS, or malaria. The high burden of both communicable and non-communicable foodborne diseases requires countries to prioritise developing strategies to improve the safety of the food supply. WHO.

We investigate non-parametric estimation of a monotone baseline hazard and a decreasing baseline density within the Cox model. Two estimators of a non-decreasing baseline hazard function are proposed. We derive the non-parametric maximum likelihood estimator and consider a Grenander type estimator, defined as the left-hand slope of the greatest convex minorant of the Breslow estimator. We demonstrate that the two estimators are strongly consistent and asymptotically equivalent and derive their common limit distribution at a fixed point. Both estimators of a non-increasing baseline hazard and their asymptotic properties are obtained in a similar manner. Furthermore, we introduce a Grenander type estimator for a non-increasing baseline density, defined as the left-hand slope of the least concave majorant of an estimator of the baseline cumulative distribution function, derived from the Breslow estimator. We show that this estimator is strongly consistent and derive its asymptotic distribution at a fixed point.

Over 10 years ago, WHO estimates of hazard-specific foodborne disease burdens showed that parasites exert considerable health burdens globally. We updated these estimates, focusing on 14 invasive parasitic diseases. Incidences, deaths, and disability-adjusted life-year (DALY) burdens were estimated for each parasitic disease from 2000 to 2021, using data from systematic reviews and the Global Burden of Diseases, Injuries, and Risk Factors Study. For some diseases, structured expert judgement was used to estimate proportions of foodborne infection. Data were pooled via hierarchical meta-regression models with uncertainty propagated through Monte Carlo simulations following disease-specific computational models defined by incidence rates and probability parameters. We estimated that 277 million illnesses were caused by potentially foodborne invasive parasites, with approximately 171 million attributable to foodborne transmission. Considerable heterogeneity by parasite, in magnitude and uncertainty, was observed. Of 4·89 million foodborne DALYs associated with these diseases, highest contributions were from Taenia solium (1·3 million) and Clonorchis sinensis (0·921 million), both also associated with most foodborne deaths. Burden was greatest in the region of the Americas, predominantly due to Chagas disease, followed by the African region, where neurocysticercosis-associated epilepsy caused most burden. Burdens decreased globally from 2000 to 2021, except in the Western Pacific region, where the burden, predominantly associated with clonorchiasis, is rising. Foodborne parasitoses cause considerable suffering, with some populations and regions particularly at risk. These data provide a baseline by which effects of interventions can be assessed and emphasis directed to those parasites exerting the greatest burden. WHO.

Like other cities in the Kathmandu Valley, Bhaktapur faces rapid urbanisation and population growth. Rivers are negatively impacted by uncontrolled settlements in flood-prone areas, lowering permeability, decreasing channels widths, and waste blockage. All these issues, along with more extreme rain events during the monsoon due to climate change, have led to increased flooding in Bhaktapur, especially by the Hanumante River. For a better understanding of flood risk, the first step is a return level analysis. For this, historical data are essential. Unfortunately, historical records of water levels are non-existent for the Hanumante River. We measured water levels and discharge on a regular basis starting from the 2019 monsoon (i.e., June). To reconstruct the missing historical data needed for a return level analysis, this research introduces the Classical Model for Structured Expert Judgment (SEJ). By employing SEJ, we were able to reconstruct historical water level data. Expert assessments were validated using the limited data available. Based on the reconstructed data, it was possible to estimate the return periods of extreme water levels of the Hanumante River by fitting a Generalized Extreme Value (GEV) distribution. Using this distribution, we estimated that a water level of about 3.5 m has a return period of ten years. This research showed that, despite considerable uncertainty in the results, the SEJ method has potential for return level analyses.

Foodborne diseases cause significant illness and death globally. We updated WHO estimates of the burden caused by diarrhoeal hazards commonly transmitted by food: Campylobacter jejuni, Campylobacter coli, and other thermotolerant Campylobacter species; Cryptosporidium spp; Cyclospora cayetanensis; Entamoeba histolytica; enteroaggregative Escherichia coli; enteropathogenic E coli; enterotoxigenic E coli; Giardia duodenalis; norovirus; rotavirus; non-typhoidal Salmonella enterica; Shiga toxin-producing E coli; Shigella spp; and Vibrio cholerae. We estimated illnesses, deaths, and disability-adjusted life-years (DALYs) for 194 countries for the period 2000-21 using data from systematic reviews; the Global Burden of Diseases, Injuries, and Risk Factors Study 2021; a structured expert judgement study; and country consultations. We used disease-specific computational models, and a hierarchical meta-regression model with geographical clustering, a global linear time trend, and uncertainty propagation. In 2021, the 14 diarrhoeal hazards caused 666 million (95% UI 483-884) illnesses, 265 000 deaths (196 000-351 000), and 15·2 million (11·6-19·1) DALYs from foodborne transmission. Shigella spp, Campylobacter, and rotavirus caused the most DALYs from foodborne transmission. The greatest burden was in the African region (773·5 DALYs [95% UI 559·7-1033·3] per 100 000 population due to foodborne transmission). Mortality rates were 7·1 times higher and DALY rates 18·9 times higher in children younger than 5 years than in people aged 5 years or older. While the overall foodborne burden decreased between 2000 (692·3 DALYs [517·9-938·1] per 100 000 population) and 2021 (193·6 [147·2-243·0] per 100 000), this trend was not consistent for all hazards. Diarrhoeal hazards continue to cause a substantial foodborne disease burden, despite decreases over time. Children in low-income countries bear the greatest burden. Prevention requires concerted efforts, including expanding global diarrhoeal disease prevention efforts beyond water, sanitation, and hygiene and vaccination to include improvements in the safety of the food supply. WHO.