The dynamics of measles in sub-Saharan Africa

Estimating the Impact of Vaccination Campaigns on Measles Transmission in Somalia

Somalia is a complex and fragile setting with a demonstrated potential for disruptive, high-burden measles outbreaks. In response, since 2018, Somalian authorities have partnered with UNICEF and the WHO to implement measles vaccination campaigns across the country. In this paper, we create a Somalia-specific model of measles transmission based on a comprehensive epidemiological dataset including case-based surveillance, vaccine registries, and serological surveys. We use this model to assess the impact of these campaign interventions on Somalian's measles susceptibility, showing, for example, that across the roughly 10 million doses delivered, 1 of every 5 immunized a susceptible child. Finally, we use the model to explore a counter-factual epidemiology without the 2019-2020 campaigns, and we estimate that those interventions prevented over 10,000 deaths.

Trends of measles in Tanzania: A 5-year review of case-based surveillance data, 2018-2022

OBJECTIVES

Tanzania observed a gradual increase in the number of measles cases since 2019 with a large outbreak recorded during 2022. This study describes the trend of measles in Tanzania over a 5-year period from 2018-2022.

METHODS

This was a descriptive study conducted using routine measles case-based surveillance system including 195 councils of the United Republic of Tanzania.

RESULTS

Between 2018 and 2022 there were 12,253 measles cases reported. Out of 10,691 (87.25%) samples tested by enzyme-linked immunosorbent assay, 903 (8.4%) were measles immunoglobulin M positive. The highest number of laboratory-confirmed measles cases was in 2022 (64.8%), followed by 2020 (13.8%), and 2019 (13.5%). Out of 1279 unvaccinated cases, 213 (16.7%) were laboratory-confirmed measles cases compared to 77/723 (10.6%) who were partially vaccinated and 71/1121 (6.3%) who were fully vaccinated (P < 0.001). Children aged between 1-4 years constituted the most confirmed measles cases after laboratory testing, followed by those aged 5-9 years. There was a notable increase in the number of laboratory-confirmed measles cases in children <1 year and 10-14 years during 2022 compared to previous years. The vaccination coverage of the first dose of measles-containing vaccine (MCV1) was maintained >90% since 2013 while MCV2 increased gradually reaching 88% in 2022.

CONCLUSIONS

Accumulation of susceptible children to measles due to suboptimal measles vaccination coverage over the years has resulted in an increase in the number of laboratory-confirmed measles cases in Tanzania with more cases recorded during the COVID-19 pandemic. Strengthening surveillance, routine immunization, and targeted strategies are key to achieving the immunity levels required to interrupt measles outbreaks.

Stabilizing the return to normal behavior in an epidemic

Predicting the interplay between infectious disease and behavior has been an intractable problem because behavioral response is so varied. We introduce a general framework for feedback between incidence and behavior for an infectious disease. By identifying stable equilibria, we provide policy end-states that are self-managing and self-maintaining. We prove mathematically the existence of two new endemic equilibria depending on the vaccination rate: one in the presence of low vaccination but with reduced societal activity (the "new normal"), and one with return to normal activity but with vaccination rate below that required for disease elimination. This framework allows us to anticipate the long-term consequence of an emerging disease and design a vaccination response that optimizes public health and limits societal consequences.

Association of public health and social measures on the hand-foot-mouth epidemic in South Korea

BACKGROUND

School based-measures such as school closure and school holidays have been considered a viable intervention during the hand-foot-mouth disease (HFMD) epidemic. The aim of this study was to explore the association of nationwide public health and social measures (PHSMs) including planned school vacation on the transmissibility and attack rate of the HFMD epidemic in South Korea.

METHODS

In this study, we used Korean national surveillance data on HFMD from 2014 to 2019 to estimate the temporal changes in HFMD transmissibility (instantaneous reproductive number, Rt). Furthermore, to assess the changes in the HFMD attack rate, we used a stochastic transmission model to simulate the HFMD epidemic with no school vacation and nationwide PHSMs in 2015 South Korea.

RESULTS

We found that school vacations and 2015 PHSMs were associated with the reduced Rt by 2-7 % and 13 %, respectively. Model projections indicated school vacations and 2015 PHSMs were associated with reduced HFMD attack rate by an average of 1.10 % (range: 0.38-1.51 %).

CONCLUSIONS

PHSMs likely have a larger association with reduced HFMD transmissibility than school-based measures alone (i.e. school vacations). Preventive measures targeting preschoolers could be considered as potential options for reducing the future burden of HFMD.

Impact of disruptions to routine vaccination programs, quantifying burden of measles, and mapping targeted supplementary immunization activities

Measles is a highly transmissible disease that requires high levels of vaccination coverage for control and elimination. Areas that are unable to achieve and maintain high coverage levels are at risk for measles outbreaks resulting in increased morbidity and mortality. Public health emergencies, such as the current COVID-19 pandemic, pose a threat to the functioning of health systems by disrupting immunization services which can derail measles vaccination efforts. Efforts to bridge coverage gaps in immunization include the rapid return to fully functioning services as well as deploying supplementary immunization activities (SIAs), which are additional vaccination campaigns intended to catch-up children who have missed routine services. However, SIAs, which to date tend to be national efforts, can be difficult to mobilize quickly, resource-intensive, and even more challenging to deploy during a public health crisis. By mapping expected burden of measles, more effective SIAs that are setting-specific and resource-efficient can be planned and mobilized. Using a spatial transmission model of measles dynamics, we projected and estimated the expected burden of national and local measles outbreaks in Zambia with the current COVID-19 pandemic as a framework to inform disruptions to routine vaccination. We characterize the impact of disruptions to routine immunization services on measles incidence, map expected case burden, and explore SIA strategies to mitigate measles outbreaks. We find that disruptions lasting six months or longer as well as having low MCV1 coverage prior to disruptions resulted in an observable increase of measles cases across provinces. Targeting provinces at higher risk of measles outbreaks for SIAs is an effective strategy to curb measles virus incidence following disruptions to routine immunization services.

Disease Progression, Clinical Features, and Risk Factors for Pneumonia in Unvaccinated Children and Adolescents with Measles: A Re-Emerging Disease in Romania

Measles causes in vaccinated children, with some exceptions, a mild disease, while the unvaccinated can suffer complications that result in serious consequences and even death. Although the introduction of the measles vaccine has reduced the number of cases and the viral spread, the current downward vaccination trend has resulted in the resurgence of the disease. Currently, Romania has a measles vaccination coverage below the 95% safety threshold. Thus, an outbreak started in 2016 and still ongoing in Romania, many cases being identified in the Western region in the pediatric population. Our objective was to conduct a thorough examination of clinical characteristics, evolution, and risk factors in vaccinated and unvaccinated children in this region. To reach our objectives we used a retrospective cohort analysis. The authors reviewed clinical and laboratory data from patients hospitalized at "Victor Babes" Hospital for Infectious Diseases and Pulmonology in Timisoara. We found a total of 136 qualifying cases of measles among the children admitted to this facility. The two comparison groups consisted of 104 children under 10 years and 32 patients between 10 and 18 years. An important characteristic of both study groups was the high prevalence of patients from the Roma ethnicity, which, although represents a minority in Romania, the prevalence was over 40% in the current study. The infection source was in 40.4% of children under 10 years inside the family, while 71.9% of infections in the group of adolescents were isolated (p-value = 0.047). The multivariate risk factor analysis identified as independent risk factors for the development of pneumonia the older age of patients (OR = 1.62), poor nutritional status (OR = 1.25), Roma ethnicity (OR = 2.44), presence of anemia (OR = 1.58), and procalcitonin (OR = 3.09). It is essential to handle these risk factors in a patient with measles, especially in conjunction with an unknown vaccination status. To achieve a vaccination rate greater than 95 percent for Romanian children, measles vaccination awareness must be promoted, moreover in the Roma population. More comprehensive preventative methods must be developed promptly with the objective of eradicating measles in Romania via a vigorous vaccination campaign.

Comparing and linking machine learning and semi-mechanistic models for the predictability of endemic measles dynamics

Measles is one the best-documented and most-mechanistically-studied non-linear infectious disease dynamical systems. However, systematic investigation into the comparative performance of traditional mechanistic models and machine learning approaches in forecasting the transmission dynamics of this pathogen are still rare. Here, we compare one of the most widely used semi-mechanistic models for measles (TSIR) with a commonly used machine learning approach (LASSO), comparing performance and limits in predicting short to long term outbreak trajectories and seasonality for both regular and less regular measles outbreaks in England and Wales (E&W) and the United States. First, our results indicate that the proposed LASSO model can efficiently use data from multiple major cities and achieve similar short-to-medium term forecasting performance to semi-mechanistic models for E&W epidemics. Second, interestingly, the LASSO model also captures annual to biennial bifurcation of measles epidemics in E&W caused by susceptible response to the late 1940s baby boom. LASSO may also outperform TSIR for predicting less-regular dynamics such as those observed in major cities in US between 1932–45. Although both approaches capture short-term forecasts, accuracy suffers for both methods as we attempt longer-term predictions in highly irregular, post-vaccination outbreaks in E&W. Finally, we illustrate that the LASSO model can both qualitatively and quantitatively reconstruct mechanistic assumptions, notably susceptible dynamics, in the TSIR model. Our results characterize the limits of predictability of infectious disease dynamics for strongly immunizing pathogens with both mechanistic and machine learning models, and identify connections between these two approaches.

Machine learning techniques in infectious disease modeling have grown in popularity in recent years. However, systematic investigation into the comparative performance of these approaches with traditional mechanistic models are still rare. In this paper, we compare one of the most widely used semi-mechanistic models for measles (TSIR) with a commonly used machine learning approach (LASSO), comparing performance and limits in predicting short to long term outbreaks of measles, one of the best-documented and most-mechanistically-studied non-linear infectious disease dynamical systems. Our results show that in general the LASSO outperform TSIR for predicting less-regular dynamics, and it can achieve similar performance in other scenarios when compared to the TSIR. The LASSO also has the advantages of not requiring explicit demographic data in model training. Finally, we identify connections between these two approaches and show that the LASSO model can both qualitatively and quantitatively reconstruct mechanistic assumptions, notably susceptible dynamics, in the TSIR model.

Climate change and health in Southeast Asia - defining research priorities and the role of the Wellcome Trust Africa Asia Programmes

This article summarises a recent virtual meeting organised by the Oxford University Clinical Research Unit in Vietnam on the topic of climate change and health, bringing local partners, faculty and external collaborators together from across the Wellcome and Oxford networks. Attendees included invited local and global climate scientists, clinicians, modelers, epidemiologists and community engagement practitioners, with a view to setting priorities, identifying synergies and fostering collaborations to help define the regional climate and health research agenda. In this summary paper, we outline the major themes and topics that were identified and what will be needed to take forward this research for the next decade. We aim to take a broad, collaborative approach to including climate science in our current portfolio where it touches on infectious diseases now, and more broadly in our future research directions. We will focus on strengthening our research portfolio on climate-sensitive diseases, and supplement this with high quality data obtained from internal studies and external collaborations, obtained by multiple methods, ranging from traditional epidemiology to innovative technology and artificial intelligence and community-led research. Through timely agenda setting and involvement of local stakeholders, we aim to help support and shape research into global heating and health in the region.

Climate change and health in Southeast Asia - defining research priorities and the role of the Wellcome Trust Africa Asia Programmes

This article summarises a recent virtual meeting organised by the Oxford University Clinical Research Unit in Vietnam on the topic of climate change and health, bringing local partners, faculty and external collaborators together from across the Wellcome and Oxford networks. Attendees included invited local and global climate scientists, clinicians, modelers, epidemiologists and community engagement practitioners, with a view to setting priorities, identifying synergies and fostering collaborations to help define the regional climate and health research agenda. In this summary paper, we outline the major themes and topics that were identified and what will be needed to take forward this research for the next decade. We aim to take a broad, collaborative approach to including climate science in our current portfolio where it touches on infectious diseases now, and more broadly in our future research directions. We will focus on strengthening our research portfolio on climate-sensitive diseases, and supplement this with high quality data obtained from internal studies and external collaborations, obtained by multiple methods, ranging from traditional epidemiology to innovative technology and artificial intelligence and community-led research. Through timely agenda setting and involvement of local stakeholders, we aim to help support and shape research into global heating and health in the region.

Oscillatory properties of class C notifiable infectious diseases in China from 2009 to 2021

Background

Epidemics of infectious diseases have a great negative impact on people's daily life. How it changes over time and what kind of laws it obeys are important questions that researchers are always interested in. Among the characteristics of infectious diseases, the phenomenon of recrudescence is undoubtedly of great concern. Understanding the mechanisms of the outbreak cycle of infectious diseases could be conducive for public health policies to the government.

Method

In this study, we collected time-series data for nine class C notifiable infectious diseases from 2009 to 2021 using public datasets from the National Health Commission of China. Oscillatory power of each infectious disease was captured using the method of the power spectrum analysis.

Results

We found that all the nine class C diseases have strong oscillations, which could be divided into three categories according to their oscillatory frequencies each year. Then, we calculated the oscillation power and the average number of infected cases of all nine diseases in the first 6 years (2009-2015) and the next 6 years (2015-2021) since the update of the surveillance system. The change of oscillation power is positively correlated to the change in the number of infected cases. Moreover, the diseases that break out in summer are more selective than those in winter.

Conclusion

Our results enable us to better understand the oscillation characteristics of class C infectious diseases and provide guidance and suggestions for the government's prevention and control policies.

Anticipating infectious disease re-emergence and elimination: a test of early warning signals using empirically based models

Timely forecasts of the emergence, re-emergence and elimination of human infectious diseases allow for proactive, rather than reactive, decisions that save lives. Recent theory suggests that a generic feature of dynamical systems approaching a tipping point-early warning signals (EWS) due to critical slowing down (CSD)-can anticipate disease emergence and elimination. Empirical studies documenting CSD in observed disease dynamics are scarce, but such demonstration of concept is essential to the further development of model-independent outbreak detection systems. Here, we use fitted, mechanistic models of measles transmission in four cities in Niger to detect CSD through statistical EWS. We find that several EWS accurately anticipate measles re-emergence and elimination, suggesting that CSD should be detectable before disease transmission systems cross key tipping points. These findings support the idea that statistical signals based on CSD, coupled with decision-support algorithms and expert judgement, could provide the basis for early warning systems of disease outbreaks.

COVID-19 Pandemic Data Modeling in Pakistan Using Time-Series SIR

Pakistan is currently facing the fourth wave of the deadly coronavirus, which was first reported in Wuhan, China, in December 2019. This work utilizes the epidemiological models to analyze Pakistan's COVID-19 data. The basic susceptible, infected, and recovered (SIR) model is studied assuming Bayesian and time-series SIR (tSIR) approaches. Many studies have been conducted from different perspectives, but to the best of our knowledge, no study is available using the SIR models for Pakistan. The coronavirus incubation period has been set to 14 days across the globe; however, this study noticed that the assumption of 14 days is not suitable for Pakistan's data. Furthermore, on the basis of R₀, we infer that COVID-19 is not a pandemic in Pakistan, as it was in other nations, such as the United States, India, Brazil, and Italy, among others. We attribute this to the best strategy adopted by the Government of Pakistan to minimize the burden of COVID-19 cases in Pakistani hospitals. It is also noticed that the posterior-based SIR (pSIR) model with uniform prior toR₀ and Poisson distribution (of log-likelihood) provides better results as compared to other distributions. From time-series SIR (tSIR), we observed that the value of the reporting rate (ρ) is less than 1 which means that cases are underreported.

Chaos is not rare in natural ecosystems

Chaotic dynamics are thought to be rare in natural populations but this may be due to methodological and data limitations, rather than the inherent stability of ecosystems. Following extensive simulation testing, we applied multiple chaos detection methods to a global database of 172 population time series and found evidence for chaos in >30%. In contrast, fitting traditional one-dimensional models identified <10% as chaotic. Chaos was most prevalent among plankton and insects and least among birds and mammals. Lyapunov exponents declined with generation time and scaled as the -1/6 power of body mass among chaotic populations. These results demonstrate that chaos is not rare in natural populations, indicating that there may be intrinsic limits to ecological forecasting and cautioning against the use of steady-state approaches to conservation and management.

Urban Scaling of Health Outcomes: a Scoping Review

Urban scaling is a framework that describes how city-level characteristics scale with variations in city size. This scoping review mapped the existing evidence on the urban scaling of health outcomes to identify gaps and inform future research. Using a structured search strategy, we identified and reviewed a total of 102 studies, a majority set in high-income countries using diverse city definitions. We found several historical studies that examined the dynamic relationships between city size and mortality occurring during the nineteenth and early twentieth centuries. In more recent years, we documented heterogeneity in the relation between city size and health. Measles and influenza are influenced by city size in conjunction with other factors like geographic proximity, while STIs, HIV, and dengue tend to occur more frequently in larger cities. NCDs showed a heterogeneous pattern that depends on the specific outcome and context. Homicides and other crimes are more common in larger cities, suicides are more common in smaller cities, and traffic-related injuries show a less clear pattern that differs by context and type of injury. Future research should aim to understand the consequences of urban growth on health outcomes in low- and middle-income countries, capitalize on longitudinal designs, systematically adjust for covariates, and examine the implications of using different city definitions.

Changes in temporal properties for epidemics of notifiable infectious diseases in China during the COVID-19 epidemic: population-based surveillance study

BACKGROUND

The COVID-19 was first reported in 2019, and the Chinese government immediately carried out stringent and effective control measures in response to the epidemics.

OBJECTIVE

These non-pharmaceutical interventions may have impacted incidences of other infectious diseases as well. Potential explanations underlying this reduction, however, are not clear. Hence, in this study, we aimed to study the influence of the COVID-19 prevention policies on other infectious diseases (mainly class B infectious diseases) in China.

METHODS

The time-series datasets between 2017 and 2021 for 23 notifiable infectious diseases were extracted from public datasets from the National Health Commission of China. Several indices (peak and trough amplitude, infection selectivity, preferred time to outbreak, oscillatory strength) of each infectious disease were calculated before and after the COVID-19 outbreak.

RESULTS

We found that the prevention and control policies for COVID-19 had a strong significant reduction effect on outbreaks of other infectious diseases. A clear event-related trough (ERT) was observed after the outbreak of COVID-19 under the strict control policies, and its decreasing amplitude is related to the infection selectivity and preferred outbreak time of the disease before the COVID-19. We also calculated the oscillatory strength before and after the COVID-19 outbreak and found that it is significantly stronger before the COVID-19 outbreak, and does not correlate with the trough amplitude.

CONCLUSIONS

Our results directly demonstrate that prevention policies for the COVID-19 have immediate additional benefits for controlling most class B infectious diseases, and several factors (infection selectivity, preferred outbreak time) may have contributed to the reduction of outbreaks. This study may guide the implementation of non-pharmaceutical interventions to control a wider range of infectious diseases.

CLINICALTRIAL

Updates on Measles Incidence and Eradication: Emphasis on the Immunological Aspects of Measles Infection

Measles is an RNA virus infectious disease mainly seen in children. Despite the availability of an effective vaccine against measles, it remains a health issue in children. Although it is a self-limiting disease, it becomes severe in undernourished and immune-compromised individuals. Measles infection is associated with secondary infections by opportunistic bacteria due to the immunosuppressive effects of the measles virus. Recent reports highlight that measles infection erases the already existing immune memory of various pathogens. This review covers the incidence, pathogenesis, measles variants, clinical presentations, secondary infections, elimination of measles virus on a global scale, and especially the immune responses related to measles infection.

Incorporating economic constraints for optimal control of immunizing infections

It is well-known that the interruption of transmission of a disease can be achieved, provided the vaccinated population reaches a threshold depending on, among others, the efficacy of vaccines. The purpose of this paper is to address the optimal vaccination strategy by imposing the economic constraints. In particular, an S--(I,V)--S model used to describe the spreading of the disease in a well-mixed population and a cost function consisting of vaccination and infection costs are proposed. The well-definedness of the above-described modeling is provided. We were then able to provide an optimal strategy to minimize the cost for all parameters. In particular, the optimal vaccination level to minimize the cost can be completely characterized for all parameters. For instance, the optimal vaccination level can be classified by the magnitude of the failure rate of the vaccine with other parameters being given. Under these circumstances, the optimal strategy to minimize the cost is roughly to eliminate the disease locally (respectively, choose an economic optimum resulting in not to wipe out the disease completely or take no vaccination for anyone) provided the vaccine failure rate is relatively small (respectively, intermediate or large). Numerical simulations to illustrate our main results are also provided. Moreover, the data collected at the height of the Covid-19 pandemic in Taiwan are also numerically simulated to provide the corresponding optimal vaccination strategy.

Does mothers' and caregivers' access to information on their child's vaccination card impact the timing of their child's measles vaccination in Uganda?

Introduction

On-time measles vaccination is essential for preventing measles infection among children as early in life as possible, especially in areas where measles outbreaks occur frequently. Characterizing the timing of routine measles vaccination (MCV1) among children and identifying risk factors for delayed measles vaccination is important for addressing barriers to recommended childhood vaccination and increasing on-time MCV1 coverage. We aim to assess the timing of children's MCV1 vaccination and to investigate the association between demographic and healthcare factors, mothers'/caregivers' ability to identify information on their child’s vaccination card, and achieving on-time (vs. delayed) MCV1 vaccination.

Methods

We conducted a population-based, door-to-door survey in Kampala, Uganda, from June–August of 2019. We surveyed mothers/caregivers of children aged one to five years to determine how familiar they were with their child’s vaccination card and to determine their child’s MCV1 vaccination status and timing. We assessed the proportion of children vaccinated for MCV1 on-time and delayed, and we evaluated the association between mothers'/caregivers' ability to identify key pieces of information (child’s birth date, sex, and MCV1 date) on their child’s vaccination card and achieving on-time MCV1 vaccination.

Results

Of the 999 mothers/caregivers enrolled, the median age was 27 years (17–50), and median child age was 29 months (12–72). Information on vaccination status was available for 66.0% (n = 659) of children. Of those who had documentation of MCV1 vaccination (n = 475), less than half (46.5%; n = 221) achieved on-time MCV1 vaccination and 53.5% (n = 254) were delayed. We found that only 47.9% (n = 264) of the 551 mothers/caregivers who were asked to identify key pieces of information on their child's vaccination card were able to identify the information, but ability to identify the key pieces of information on the card was not independently associated with achieving on-time MCV1 vaccination.

Conclusion

Mothers'/caregivers' ability to identify key pieces of information on their child’s vaccination card was not associated with achieving on-time MCV1 vaccination. Further research can shed light on interventions that may prompt or remind mothers/caregivers of the time and age when their child is due for measles vaccine to increase the chance of the child receiving it at the recommended time.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13113-z.

COVID-19, air quality and space monitoring

Due to the worldwide spread of the coronavirus disease 2019 (COVID-19), human mobility and economic activity have slowed down considerably since early 2020. A relatively high number of those infected develop serious pneumonia leading to progressive respiratory failure, system disease and often death. Apart from close human-to-human contact, the acceleration and global diffusion of this pandemic has been shown to be associated with changes in atmospheric chemistry and air pollution by microscopic particulate matter (PM). Breathing air with high concentrations of nitrogen dioxide and PM can result in over-expression of the angiotensin converting enzyme-2 (ACE-2) leading to stress of organs, such as heart and kidneys. Satellite monitoring can play a crucial role in spatio-temporal surveillance of the disease by producing data on pollution as proxy for industrial activity, transport and traffic circulation. Real-time monitoring of COVID-19 in air and chemical pollution of the atmospheric boundary layer available from Earth-observing satellites commuting with Health Information Systems (HIS) would be useful for decision makers involved with public health.

Analysis and prediction of hand, foot and mouth disease incidence in China using Random Forest and XGBoost

Hand, foot and mouth disease (HFMD) is an increasingly serious public health problem, and it has caused an outbreak in China every year since 2008. Predicting the incidence of HFMD and analyzing its influential factors are of great significance to its prevention. Now, machine learning has shown advantages in infectious disease models, but there are few studies on HFMD incidence based on machine learning that cover all the provinces in mainland China. In this study, we proposed two different machine learning algorithms, Random Forest and eXtreme Gradient Boosting (XGBoost), to perform our analysis and prediction. We first used Random Forest to examine the association between HFMD incidence and potential influential factors for 31 provinces in mainland China. Next, we established Random Forest and XGBoost prediction models using meteorological and social factors as the predictors. Finally, we applied our prediction models in four different regions of mainland China and evaluated the performance of them. Our results show that: 1) Meteorological factors and social factors jointly affect the incidence of HFMD in mainland China. Average temperature and population density are the two most significant influential factors; 2) Population flux has different delayed effect in affecting HFMD incidence in different regions. From a national perspective, the model using population flux data delayed for one month has better prediction performance; 3) The prediction capability of XGBoost model was better than that of Random Forest model from the overall perspective. XGBoost model is more suitable for predicting the incidence of HFMD in mainland China.

Measles outbreak investigation in Aweil East county, South Sudan

During January 2018-June 2020, Aweil East confirmed five measles outbreaks. In March 2020, Aweil East reported twenty measles IgM+ cases. Before this outbreak, Aweil East had confirmed an outbreak in late November 2019. Even after conducting outbreak reactive vaccinations (ORV) in December 2019 and February 2020, measles spread was not interrupted. The nationally supported measles follow-up campaign (MFUC) conducted in late February 2020 was deferred in Aweil East because of the February ORV. We reviewed the measles data collected through passive and active surveillance. A matched case-control study was conducted to evaluate potential exposures. Face-to-face interviews with cases and controls using a semi-structured questionnaire were used to collect demographics, disease, and exposures related data. A total of 687 cases with eight deaths; attack and case fatality rate of 123/100,000 population and 1.16%, respectively. Among the cases, 51.8% were male, the median age was four years, and 59% of cases ≥9 months were unvaccinated. Eighty point six percent (80.6%) of cases reported after the February ORV were unvaccinated. The outbreak peaked in late March 2020. Unvaccinated persons had higher odds of getting measles (adjusted odds ratio (AOR)=8.569; 95% CI [1.41- 53.4], p=0.02). Non exposed persons had a lower odd of getting measles (AOR=0.114; 95% CI [0.02-0.61], p=0.011). During 2018-2019, the accumulated number of unvaccinated children (18,587) is more than a birth cohort of the county. Persistent low routine vaccination is the most critical driver of the measles outbreaks. Low-quality ORV and the intermediate population density are secondary drivers of the outbreaks.

Are there medium to short-term multifaceted effects of the airborne pollutant PM2.5 determining the emergence of SARS-CoV-2 variants?

The COVID-19 pandemic has been characterised by successiveoutbreaks effecting large swathes of the world's populations. These waves of infection have been mainly driven by a number of more transmissiblevariants which appear to evade the populations' immunity gained from previous outbreaks. There appears to be a link between COVID-19 and a ubiquitous airborne pollutant calledparticulate matter, PM2.5. Particulate matter through a number of mechanisms, including its anthropogenic effect, appears to be associated with the incidence and the mortality related to the COVID-19 pandemic. This paper poses a number of hypotheses on the short to medium-term mechanisms whereby PM2.5 may be party to the natural selection of SARS-CoV-2 virus, with the consequent emergence of variants.

Climate change and health in Southeast Asia - defining research priorities and the role of the Wellcome Trust Africa Asia Programmes

This article summarises a recent virtual meeting organised by the Oxford University Clinical Research Unit in Vietnam on the topic of climate change and health, bringing local partners, faculty and external collaborators together from across the Wellcome and Oxford networks. Attendees included invited local and global climate scientists, clinicians, modelers, epidemiologists and community engagement practitioners, with a view to setting priorities, identifying synergies and fostering collaborations to help define the regional climate and health research agenda. In this summary paper, we outline the major themes and topics that were identified and what will be needed to take forward this research for the next decade. We aim to take a broad, collaborative approach to including climate science in our current portfolio where it touches on infectious diseases now, and more broadly in our future research directions. We will focus on strengthening our research portfolio on climate-sensitive diseases, and supplement this with high quality data obtained from internal studies and external collaborations, obtained by multiple methods, ranging from traditional epidemiology to innovative technology and artificial intelligence and community-led research. Through timely agenda setting and involvement of local stakeholders, we aim to help support and shape research into global heating and health in the region.

Immunization using a heterogeneous geo-spatial population model: A qualitative perspective on COVID-19 vaccination strategies

Epidemic modeling has been a key tool for understanding the impact of global viral outbreaks for over two decades. Recent developments of the COVID-19 pandemic have accelerated research using compartmental models, like SI, SIR, SEIR, with their appropriate modifications. However, there is a large body of recent research consolidated on homogeneous population mixing models, which are known to offer reduced tractability, and render conclusions hard to quantify. As such, based on our recent work, introducing the heterogeneous geo-spatial mobility population model (GPM), we adapt a modified SIR-V (susceptible-infected-recovered-vaccinated) epidemic model which embodies the idea of patient relapse from R back to S, vaccination of R and S patients (reducing their infectiousness), thus altering the infectiousness of V patients (from λ_n to λ_r). Simulation results spanning over a period of t = 2000 days (6 years, the period « 2020-2025) compare the impact of an epidemic outbreak with variable vaccination strategies, starting after 1 year (as is the case of COVID-19). The infected proportion in the remaining 5-year period is analyzed using vaccination rates from r_v = 0 (no vaccination) to r_v = 1. While r_v < 0.4 is less effective during the earlier stages, all strategies with r_v > 0.4 show a similar downward convergence reducing the number of infected by more than half, compared to no vaccination. Given the complexity of epidemic processes, we conclude that higher vaccination rates yield similar results, but a minimal r_v = 0.4 (40% of population over five years) should be targeted.

Modelling the relative benefits of using the measles vaccine outside cold chain for outbreak response

INTRODUCTION

Rapid outbreak response vaccination is a strategy for measles control and elimination. Measles vaccines must be stored and transported within a specified temperature range, but this can present significant challenges when targeting remote populations. Measles vaccine licensure for delivery outside cold chain (OCC) could provide more vaccine transport/storage space without ice packs, and a solution to shorten response times. However, due to vaccine safety and wastage considerations, the OCC strategy will require other operational changes, potentially including the use of 1-dose (monodose) instead of 10-dose vials, requiring larger transport/storage equipment currently achieved with 10-dose vials. These trade-offs require quantitative comparisons of vaccine delivery options to evaluate their relative benefits.

METHODS

We developed a modelling framework combining elements of the vaccine supply chain - cold chain, vial, team, and transport equipment types - with a measles transmission dynamics model to compare vaccine delivery options. We compared 10 strategies resulting from combinations of the vaccine supply elements and grouped into three main classes: OCC, partial cold chain (PCC), and full cold chain (FCC). For each strategy, we explored a campaign with 20 teams sequentially targeting 5 locations with 100,000 individuals each. We characterised the time needed to freeze ice packs and complete the campaign (campaign duration), vaccination coverage, and cases averted, assuming a fixed pre-deployment delay before campaign commencement. We performed sensitivity analyses of the pre-deployment delay, population sizes, and two team allocation schemes.

RESULTS

The OCC, PCC, and FCC strategies achieve campaign durations of 50, 51, and 52 days, respectively. Nine of the ten strategies can achieve a vaccination coverage of 80%, and OCC averts the most cases.

DISCUSSION

The OCC strategy, therefore, presents improved operational and epidemiological outcomes relative to current practice and the other options considered.

Practical geospatial and sociodemographic predictors of human mobility

Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.

A novel geo-hierarchical population mobility model for spatial spreading of resurgent epidemics

Computational models for large, resurgent epidemics are recognized as a crucial tool for predicting the spread of infectious diseases. It is widely agreed, that such models can be augmented with realistic multiscale population models and by incorporating human mobility patterns. Nevertheless, a large proportion of recent studies, aimed at better understanding global epidemics, like influenza, measles, H1N1, SARS, and COVID-19, underestimate the role of heterogeneous mixing in populations, characterized by strong social structures and geography. Motivated by the reduced tractability of studies employing homogeneous mixing, which make conclusions hard to deduce, we propose a new, very fine-grained model incorporating the spatial distribution of population into geographical settlements, with a hierarchical organization down to the level of households (inside which we assume homogeneous mixing). In addition, population is organized heterogeneously outside households, and we model the movement of individuals using travel distance and frequency parameters for inter- and intra-settlement movement. Discrete event simulation, employing an adapted SIR model with relapse, reproduces important qualitative characteristics of real epidemics, like high variation in size and temporal heterogeneity (e.g., waves), that are challenging to reproduce and to quantify with existing measures. Our results pinpoint an important aspect, that epidemic size is more sensitive to the increase in distance of travel, rather that the frequency of travel. Finally, we discuss implications for the control of epidemics by integrating human mobility restrictions, as well as progressive vaccination of individuals.

Low levels of respiratory syncytial virus activity in Europe during the 2020/21 season: what can we expect in the coming summer and autumn/winter?

Since the introduction of non-pharmacological interventions to control COVID-19, respiratory syncytial virus (RSV) activity in Europe has been limited. Surveillance data for 17 countries showed delayed RSV epidemics in France (≥ 12 w) and Iceland (≥ 4 w) during the 2020/21 season. RSV cases (predominantly small children) in France and Iceland were older compared with previous seasons. We hypothesise that future RSV epidemic(s) could start outside the usual autumn/winter season and be larger than expected. Year-round surveillance of RSV is of critical importance.

Measles epidemiology and vaccination coverage in Oromia Region, Ethiopia: Evidence from surveillance, 2011-2018

Despite a reported high coverage of measles-containing vaccine (MCV), low-income countries including, Ethiopia, have sustained high measles transmission with frequent outbreaks. We investigated the distribution of measles infection and vaccination in Oromia Regional State, Ethiopia. According to the World Health Organization (WHO) and the Ethiopian measles case classification guidelines, measles cases were classified as laboratory-confirmed, clinically compatible, and epidemiologically linked. We derived measles vaccination coverage estimates using reported measles vaccine efficacy and, the proportion of measles cases vaccinated with measles vaccine at least once from the surveillance data. We calculated measles effective reproduction number (R_e) in the region. Almost twenty-five thousand measles cases were reported through the surveillance system, with more than 50% of the suspected and confirmed measles cases reported in 2015. Measles had sustained and high transmission rate with uneven distribution among the zones. Children between 1 and 4 years of age and MCV unvaccinated individuals were the most affected groups. In all the zones, the average surveillance-estimated MCV coverage among both infants and under-five children was significantly lower than the WHO recommended minimum 90% threshold herd-immunity. With this level of vaccination coverage, an infected case can transmit to more than four individuals. Nevertheless, the administrative coverage reports for the concurrent period were consistently above 90%. The estimated MCV coverage across the Oromia region was well below the recommended herd-immunity threshold. It partly explains the apparent mismatch of sustained measles transmission and outbreaks despite the very high administrative coverage estimates. Oromia regional health bureau, in collaboration with key stakeholders, should make a concerted effort to increase the effective-coverage of MCV to at least 90%. Additionally, multiple-dose MCV has to be scaled-up and accompanied with appropriate geographic and age targeting using evidence from surveillance data. Immediate programmatic action is needed to improve the quality of measles surveillance.

Enlightenment on oscillatory properties of 23 class B notifiable infectious diseases in the mainland of China from 2004 to 2020

A variety of infectious diseases occur in mainland China every year. Cyclic oscillation is a widespread attribute of most viral human infections. Understanding the outbreak cycle of infectious diseases can be conducive for public health management and disease surveillance. In this study, we collected time-series data for 23 class B notifiable infectious diseases from 2004 to 2020 using public datasets from the National Health Commission of China. Oscillatory properties were explored using power spectrum analysis. We found that the 23 class B diseases from the dataset have obvious oscillatory patterns (seasonal or sporadic), which could be divided into three categories according to their oscillatory power in different frequencies each year. These diseases were found to have different preferred outbreak months and infection selectivity. Diseases that break out in autumn and winter are more selective. Furthermore, we calculated the oscillation power and the average number of infected cases of all 23 diseases in the first eight years (2004 to 2012) and the next eight years (2012 to 2020) since the update of the surveillance system. A strong positive correlation was found between the change of oscillation power and the change in the number of infected cases, which was consistent with the simulation results using a conceptual hybrid model. The establishment of reliable and effective analytical methods contributes to a better understanding of infectious diseases’ oscillation cycle characteristics. Our research has certain guiding significance for the effective prevention and control of class B infectious diseases.

Could the COVID-19 Positive Asymptomatic Tobacco Smoker be a Silent Superspeader?

There appears to be a connection between COVID-19 infection and an airborne microscopic pollutant called particulate matter which has been suggested to act as vector for viral transmission. The highest human exposure to particulate matter occurs during smoking and to a lesser extent to 2^nd hand smoking. This article offers a hypothetical proposition that particulate matter derived from tobacco smoking may act as COVID-19’s vector for infection transmission. With a background smoking Chinese male population of more than 66% and more than 70% of Chinese nonsmokers exposed to 2^nd hand smoke, the potential of exhaled smoke acting as a viral vector is significant. If this hypothesis is proven, measures such as face protection to reduce coronavirus-laden particulate matter transmission, measures of social distancing and legislation to protect nonsmokers from contracting the infection through 2^nd hand smoking should be implemented. (www.actabiomedica.it)

Epidemiological dynamics of enterovirus D68 in the United States and implications for acute flaccid myelitis

Acute flaccid myelitis (AFM) recently emerged in the United States as a rare but serious neurological condition since 2012. Enterovirus D68 (EV-D68) is thought to be a main causative agent, but limited surveillance of EV-D68 in the United States has hampered the ability to assess their causal relationship. Using surveillance data from the BioFire Syndromic Trends epidemiology network in the United States from January 2014 to September 2019, we characterized the epidemiological dynamics of EV-D68 and found latitudinal gradient in the mean timing of EV-D68 cases, which are likely climate driven. We also demonstrated a strong spatiotemporal association of EV-D68 with AFM. Mathematical modeling suggested that the recent dominant biennial cycles of EV-D68 dynamics may not be stable. Nonetheless, we predicted that a major EV-D68 outbreak, and hence an AFM outbreak, would have still been possible in 2020 under normal epidemiological conditions. Nonpharmaceutical intervention efforts due to the ongoing COVID-19 pandemic are likely to have reduced the sizes of EV-D68 and AFM outbreaks in 2020, illustrating the broader epidemiological impact of the pandemic.

Detection of SARS-CoV-2 DNA Targets Using Femtoliter Optofluidic Waveguides

Chip-scale SARS-CoV-2 testing was demonstrated using silicon nitride (Si3N4) nanoslot fluidic waveguides to detect a tagged oligonucleotide with a coronavirus DNA sequence. The slot waveguides were fabricated using complementary metal-oxide-semiconductor (CMOS) fabrication processes, including multiscale lithography and selective reactive ion etching (RIE), forming femtoliter fluidic channels. Finite difference method (FDM) simulation was used to calculate the optical field distribution of the waveguide mode when the waveguide sensor was excited by transverse electric (TE) and transverse magnetic (TM) polarized light. For the TE polarization, a strong optical field was created in the slot region and its field intensity was 14× stronger than the evanescent sensing field from the TM polarization. The nanoscale confinement of the optical sensing field significantly enhanced the light-analyte interaction and improved the optical sensitivity. The sensitivity enhancement was experimentally demonstrated by measuring the polarization-dependent fluorescence emission from the tagged oligonucleotide. The photonic chips consisting of femtoliter Si3N4 waveguides provide a low-cost and high throughput platform for real-time virus identification, which is critical for point-of-care (PoC) diagnostic applications.

Patterns and processes of pathogen exposure in gray wolves across North America

The presence of many pathogens varies in a predictable manner with latitude, with infections decreasing from the equator towards the poles. We investigated the geographic trends of pathogens infecting a widely distributed carnivore: the gray wolf (Canis lupus). Specifically, we investigated which variables best explain and predict geographic trends in seroprevalence across North American wolf populations and the implications of the underlying mechanisms. We compiled a large serological dataset of nearly 2000 wolves from 17 study areas, spanning 80° longitude and 50° latitude. Generalized linear mixed models were constructed to predict the probability of seropositivity of four important pathogens: canine adenovirus, herpesvirus, parvovirus, and distemper virus-and two parasites: Neospora caninum and Toxoplasma gondii. Canine adenovirus and herpesvirus were the most widely distributed pathogens, whereas N. caninum was relatively uncommon. Canine parvovirus and distemper had high annual variation, with western populations experiencing more frequent outbreaks than eastern populations. Seroprevalence of all infections increased as wolves aged, and denser wolf populations had a greater risk of exposure. Probability of exposure was positively correlated with human density, suggesting that dogs and synanthropic animals may be important pathogen reservoirs. Pathogen exposure did not appear to follow a latitudinal gradient, with the exception of N. caninum. Instead, clustered study areas were more similar: wolves from the Great Lakes region had lower odds of exposure to the viruses, but higher odds of exposure to N. caninum and T. gondii; the opposite was true for wolves from the central Rocky Mountains. Overall, mechanistic predictors were more informative of seroprevalence trends than latitude and longitude. Individual host characteristics as well as inherent features of ecosystems determined pathogen exposure risk on a large scale. This work emphasizes the importance of biogeographic wildlife surveillance, and we expound upon avenues of future research of cross-species transmission, spillover, and spatial variation in pathogen infection.

Epidemiological dynamics of enterovirus D68 in the US: implications for acute flaccid myelitis

The lack of active surveillance for enterovirus D68 (EV-D68) in the US has hampered the ability to assess the relationship with predominantly biennial epidemics of acute flaccid myelitis (AFM), a rare but serious neurological condition. Using novel surveillance data from the BioFire® Syndromic Trends (Trend) epidemiology network, we characterize the epidemiological dynamics of EV-D68 and demonstrate strong spatiotemporal association with AFM. Although the recent dominant biennial cycles of EV-D68 dynamics may not be stable, we show that a major EV-D68 epidemic, and hence an AFM outbreak, would still be possible in 2020 under normal epidemiological conditions. Significant social distancing due to the ongoing COVID-19 pandemic could reduce the size of an EV-D68 epidemic in 2020, illustrating the potential broader epidemiological impact of the pandemic.

Vaccination strategies for measles control and elimination: time to strengthen local initiatives

BACKGROUND

Through a combination of strong routine immunization (RI), strategic supplemental immunization activities (SIA) and robust surveillance, numerous countries have been able to approach or achieve measles elimination. The fragility of these achievements has been shown, however, by the resurgence of measles since 2016. We describe trends in routine measles vaccine coverage at national and district level, SIA performance and demographic changes in the three regions with the highest measles burden.

FINDINGS

WHO-UNICEF estimates of immunization coverage show that global coverage of the first dose of measles vaccine has stabilized at 85% from 2015 to 19. In 2000, 17 countries in the WHO African and Eastern Mediterranean regions had measles vaccine coverage below 50%, and although all increased coverage by 2019, at a median of 60%, it remained far below levels needed for elimination. Geospatial estimates show many low coverage districts across Africa and much of the Eastern Mediterranean and southeast Asian regions. A large proportion of children unvaccinated for MCV live in conflict-affected areas with remote rural areas and some urban areas also at risk. Countries with low RI coverage use SIAs frequently, yet the ideal timing and target age range for SIAs vary within countries, and the impact of SIAs has often been mitigated by delays or disruptions. SIAs have not been sufficient to achieve or sustain measles elimination in the countries with weakest routine systems. Demographic changes also affect measles transmission, and their variation between and within countries should be incorporated into strategic planning.

CONCLUSIONS

Rebuilding services after the COVID-19 pandemic provides a need and an opportunity to increase community engagement in planning and monitoring services. A broader suite of interventions is needed beyond SIAs. Improved methods for tracking coverage at the individual and community level are needed together with enhanced surveillance. Decision-making needs to be decentralized to develop locally-driven, sustainable strategies for measles control and elimination.

COVID-19 cycles and rapidly evaluating lockdown strategies using spectral analysis

Spectral analysis characterises oscillatory time series behaviours such as cycles, but accurate estimation requires reasonable numbers of observations. At the time of writing, COVID-19 time series for many countries are short: pre- and post-lockdown series are shorter still. Accurate estimation of potentially interesting cycles seems beyond reach with such short series. We solve the problem of obtaining accurate estimates from short series by using recent Bayesian spectral fusion methods. We show that transformed daily COVID-19 cases for many countries generally contain three cycles operating at wavelengths of around 2.7, 4.1 and 6.7 days (weekly) and that shorter wavelength cycles are suppressed after lockdown. The pre- and post-lockdown differences suggest that the weekly effect is at least partly due to non-epidemic factors. Unconstrained, new cases grow exponentially, but the internal cyclic structure causes periodic declines. This suggests that lockdown success might only be indicated by four or more daily falls. Spectral learning for epidemic time series contributes to the understanding of the epidemic process and can help evaluate interventions. Spectral fusion is a general technique that can fuse spectra recorded at different sampling rates, which can be applied to a wide range of time series from many disciplines.

Tensor decomposition for infectious disease incidence data

Many demographic and ecological processes generate seasonal and other periodicities. Seasonality in infectious disease transmission can result from climatic forces such as temperature and humidity; variation in contact rates as a result of migration or school calendar; or temporary surges in birth rates. Seasonal drivers of acute immunizing infections can also drive longer-term fluctuations.Tensor decomposition has been used in many disciplines to uncover dominant trends in multi-dimensional data. We introduce tensors as a novel method for decomposing oscillatory infectious disease time series.We illustrate the reliability of the method by applying it to simulated data. We then present decompositions of measles data from England and Wales. This paper leverages simulations as well as much-studied data to illustrate the power of tensor decomposition to uncover dominant epidemic signals as well as variation in space and time. We then use tensor decomposition to uncover new findings and demonstrate the potential power of the method for disease incidence data. In particular, we are able to distinguish between annual and biennial signals across locations and shifts in these signals over time.Tensor decomposition is able to isolate variation in disease seasonality as a result of variation in demographic rates. The method allows us to discern variation in the strength of such signals by space and population size. Tensors provide an opportunity for a concise approach to uncovering heterogeneity in disease transmission across space and time in large datasets.

The impact of COVID-19 nonpharmaceutical interventions on the future dynamics of endemic infections

Nonpharmaceutical interventions (NPIs) have been employed to reduce the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), yet these measures are already having similar effects on other directly transmitted, endemic diseases. Disruptions to the seasonal transmission patterns of these diseases may have consequences for the timing and severity of future outbreaks. Here we consider the implications of SARS-CoV-2 NPIs for two endemic infections circulating in the United States of America: respiratory syncytial virus (RSV) and seasonal influenza. Using laboratory surveillance data from 2020, we estimate that RSV transmission declined by at least 20% in the United States at the start of the NPI period. We simulate future trajectories of both RSV and influenza, using an epidemic model. As susceptibility increases over the NPI period, we find that substantial outbreaks of RSV may occur in future years, with peak outbreaks likely occurring in the winter of 2021-2022. Longer NPIs, in general, lead to larger future outbreaks although they may display complex interactions with baseline seasonality. Results for influenza broadly echo this picture, but are more uncertain; future outbreaks are likely dependent on the transmissibility and evolutionary dynamics of circulating strains.

Long-term trends in seasonality of mortality in urban Madagascar: the role of the epidemiological transition

Background: Seasonal patterns of mortality have been identified in Sub-Saharan Africa but their changes over time are not well documented.Objective: Based on death notification data from Antananarivo, the capital city of Madagascar, this study assesses seasonal patterns of all-cause and cause-specific mortality by age groups and evaluates how these patterns changed over the period 1976-2015.Methods: Monthly numbers of deaths by cause were obtained from death registers maintained by the Municipal Hygiene Office in charge of verifying deaths before the issuance of burial permits. Generalized Additive Mixed regression models (GAMM) were used to test for seasonality in mortality and its changes over the last four decades, controlling for long-term trends in mortality.Results: Among children, risks of dying were the highest during the hot and rainy season, but seasonality in child mortality has significantly declined since the mid-1970s, as a result of declines in the burden of infectious diseases and nutritional deficiencies. In adults aged 60 and above, all-cause mortality rates are the highest in the dry and cold season, due to peaks in cardiovascular diseases, with little change over time. Overall, changes in the seasonality of all-cause mortality have been driven by shifts in the hierarchy of causes of death, while changes in the seasonality within broad categories of causes of death have been modest.Conclusion: Shifts in disease patterns brought about by the epidemiological transition, rather than changes in seasonal variation in cause-specific mortality, are the main drivers of trends in the seasonality of all-cause mortality.

Epidemiological Characteristics, Seasonal Dynamic Patterns, and Associations with Meteorological Factors of Rubella in Shaanxi Province, China, 2005-2018

Rubella occurs worldwide, causing approximately 100,000 cases annually of congenital rubella syndrome, leading to severe birth defects. Better targeting of public health interventions is needed to achieve rubella elimination goals. To that end, we measured the epidemiological characteristics and seasonal dynamic patterns of rubella and determined its association with meteorological factors in Shaanxi Province, China. Data on rubella cases in Shaanxi Province from 2005 to 2018 were obtained from the Chinese National Notifiable Disease Reporting System. The Morlet wavelet analysis was used to estimate temporal periodicity of rubella incidence. Mixed generalized additive models were used to measure associations between meteorological variables (temperature and relative humidity) and rubella incidence. A total of 17,185 rubella cases were reported in Shaanxi during the study period, for an annual incidence of 3.27 cases per 100,000 population. Interannual oscillations in rubella incidence of 0.8-1.4 years, 3.8-4.8 years, and 0.5 years were detected. Both temperature and relative humidity exhibited nonlinear associations with the incidence of rubella. The accumulative relative risk of transmission for the overall pooled estimates was maximized at a temperature of 0.23°C and relative humidity of 41.6%. This study found that seasonality and meteorological factors have impact on the transmission of rubella; public health interventions to eliminate rubella must consider periodic and seasonal fluctuations as well as meteorological factors.

Cyclic epidemics and extreme outbreaks induced by hydro-climatic variability and memory

A minimalist model of ecohydrologic dynamics is coupled to the well-known susceptible-infected-recovered epidemiological model to explore hydro-climatic controls on infection dynamics and extreme outbreaks. The resulting HYSIR model reveals the existence of a noise-induced bifurcation producing oscillations in infection dynamics. Linearization of the governing equations allows for an analytic expression for the periodicity of infections in terms of both epidemiological (e.g. transmission and recovery rate) and hydrologic (i.e. soil moisture decay rate or memory) parameters. Numerical simulations of the full stochastic, nonlinear system show extreme outbreaks in response to particular combinations of hydro-climatic conditions, neither of which is extreme per se, rather than a single major climatic event. These combinations depend on the assumed functional relationship between the hydrologic variables and the transmission rate. Our results emphasize the importance of hydro-climatic history and system memory in evaluating the risk of severe outbreaks.

Potential role of particulate matter in the spreading of COVID-19 in Northern Italy: first observational study based on initial epidemic diffusion

OBJECTIVES

A number of studies have shown that the airborne transmission route could spread some viruses over a distance of 2 meters from an infected person. An epidemic model based only on respiratory droplets and close contact could not fully explain the regional differences in the spread of COVID-19 in Italy. On March 16th 2020, we presented a position paper proposing a research hypothesis concerning the association between higher mortality rates due to COVID-19 observed in Northern Italy and average concentrations of PM₁₀ exceeding a daily limit of 50 µg/m³.

METHODS

To monitor the spreading of COVID-19 in Italy from February 24th to March 13th (the date of the Italian lockdown), official daily data for PM₁₀ levels were collected from all Italian provinces between February 9th and February 29th, taking into account the maximum lag period (14 days) between the infection and diagnosis. In addition to the number of exceedances of the daily limit value of PM₁₀, we also considered population data and daily travelling information for each province.

RESULTS

Exceedance of the daily limit value of PM₁₀ appears to be a significant predictor of infection in univariate analyses (p<0.001). Less polluted provinces had a median of 0.03 infections over 1000 residents, while the most polluted provinces showed a median of 0.26 cases. Thirty-nine out of 41 Northern Italian provinces resulted in the category with the highest PM₁₀ levels, while 62 out of 66 Southern provinces presented low PM₁₀ concentrations (p<0.001). In Milan, the average growth rate before the lockdown was significantly higher than in Rome (0.34 vs 0.27 per day, with a doubling time of 2.0 days vs 2.6, respectively), thus suggesting a basic reproductive number R₀>6.0, comparable with the highest values estimated for China.

CONCLUSION

A significant association has been found between the geographical distribution of daily PM₁₀ exceedances and the initial spreading of COVID-19 in the 110 Italian provinces.

Potential role of particulate matter in the spreading of COVID-19 in Northern Italy: first observational study based on initial epidemic diffusion

OBJECTIVES

A number of studies have shown that the airborne transmission route could spread some viruses over a distance of 2 meters from an infected person. An epidemic model based only on respiratory droplets and close contact could not fully explain the regional differences in the spread of COVID-19 in Italy. On March 16th 2020, we presented a position paper proposing a research hypothesis concerning the association between higher mortality rates due to COVID-19 observed in Northern Italy and average concentrations of PM₁₀ exceeding a daily limit of 50 µg/m³.

METHODS

To monitor the spreading of COVID-19 in Italy from February 24th to March 13th (the date of the Italian lockdown), official daily data for PM₁₀ levels were collected from all Italian provinces between February 9th and February 29th, taking into account the maximum lag period (14 days) between the infection and diagnosis. In addition to the number of exceedances of the daily limit value of PM₁₀, we also considered population data and daily travelling information for each province.

RESULTS

Exceedance of the daily limit value of PM₁₀ appears to be a significant predictor of infection in univariate analyses (p<0.001). Less polluted provinces had a median of 0.03 infections over 1000 residents, while the most polluted provinces showed a median of 0.26 cases. Thirty-nine out of 41 Northern Italian provinces resulted in the category with the highest PM₁₀ levels, while 62 out of 66 Southern provinces presented low PM₁₀ concentrations (p<0.001). In Milan, the average growth rate before the lockdown was significantly higher than in Rome (0.34 vs 0.27 per day, with a doubling time of 2.0 days vs 2.6, respectively), thus suggesting a basic reproductive number R₀>6.0, comparable with the highest values estimated for China.

CONCLUSION

A significant association has been found between the geographical distribution of daily PM₁₀ exceedances and the initial spreading of COVID-19 in the 110 Italian provinces.

Potential role of particulate matter in the spreading of COVID-19 in Northern Italy: first observational study based on initial epidemic diffusion

OBJECTIVES

A number of studies have shown that the airborne transmission route could spread some viruses over a distance of 2 meters from an infected person. An epidemic model based only on respiratory droplets and close contact could not fully explain the regional differences in the spread of COVID-19 in Italy. On March 16th 2020, we presented a position paper proposing a research hypothesis concerning the association between higher mortality rates due to COVID-19 observed in Northern Italy and average concentrations of PM10 exceeding a daily limit of 50 µg/m3.

METHODS

To monitor the spreading of COVID-19 in Italy from February 24th to March 13th (the date of the Italian lockdown), official daily data for PM10 levels were collected from all Italian provinces between February 9th and February 29th, taking into account the maximum lag period (14 days) between the infection and diagnosis. In addition to the number of exceedances of the daily limit value of PM10, we also considered population data and daily travelling information for each province.

RESULTS

Exceedance of the daily limit value of PM10 appears to be a significant predictor of infection in univariate analyses (p<0.001). Less polluted provinces had a median of 0.03 infections over 1000 residents, while the most polluted provinces showed a median of 0.26 cases. Thirty-nine out of 41 Northern Italian provinces resulted in the category with the highest PM10 levels, while 62 out of 66 Southern provinces presented low PM10 concentrations (p<0.001). In Milan, the average growth rate before the lockdown was significantly higher than in Rome (0.34 vs 0.27 per day, with a doubling time of 2.0 days vs 2.6, respectively), thus suggesting a basic reproductive number R0>6.0, comparable with the highest values estimated for China.

CONCLUSION

A significant association has been found between the geographical distribution of daily PM10 exceedances and the initial spreading of COVID-19 in the 110 Italian provinces.

Socio-demographic correlates of first dose of measles (MCV1) vaccination coverage in India

Background

Between 2010 and 2018, measles-related mortality had halved in India mainly with effective measles vaccination campaigns and widespread coverage across the states and population subgroups. Despite the commendable vaccination coverage, 2.9 million children in India missed the first dose of measles vaccine (MCV1) in 2017, and many of those vaccinated were not vaccinated at the recommended age (i.e. between 9 and 12 months). This study analyzed pattern and correlates of MCV1 coverage and MCV1 administration at recommended age among children aged 12–23 months in India.

Methods

We used the official data from the recent round of National Family Health Survey (NFHS-4), a nationally representative cross-sectional household survey in India conducted in 2015–16. Descriptive statistics and logistic regression analysis were applied to ascertain the influence of specified socio-demographic variables affecting measles vaccination coverage in India.

Results

The study revealed the distinct variations in coverage of MCV1 between the districts of India. There were also major challenges with age recommended vaccination, with about 15% of eligible children not vaccinated within the recommended age range, attributable to several socio-demographic factors. Significantly, antenatal care utilization of mothers strongly influenced MCV1 coverage and age recommended MCV1 coverage in India. The study also identified that children who missed MCV1 had one or more adverse health risks such as malnutrition, anemia and diarrhea disease.

Conclusions

A socio-economic gradient exists in India’s MCV1 coverage, mediated by antenatal visits, education of mothers, and highlighted socio-demographic factors. Infection with measles was significantly correlated with greater anthropometric deficits among the study cohort, indicating a wider range of benefits from preventing measles infection. Eliminating morbidity and mortality from measles in India is feasible, although it will require efficient expanded program on immunization management, enhanced health literacy among mothers, continuing commitment from central state and district political authorities.

Investigating persistent measles dynamics in Niger and associations with rainfall

Measles is a major cause of child mortality in sub-Saharan Africa. Current immunization strategies achieve low coverage in areas where transmission drivers differ substantially from those in high-income countries. A better understanding of measles transmission in areas with measles persistence will increase vaccination coverage and reduce ongoing transmission. We analysed weekly reported measles cases at the district level in Niger from 1995 to 2004 to identify underlying transmission mechanisms. We identified dominant periodicities and the associated spatial clustering patterns. We also investigated associations between reported measles cases and environmental drivers associated with human activities, particularly rainfall. The annual and 2-3-year periodicities dominated the reporting data spectrum. The annual periodicity was strong with contiguous spatial clustering, consistent with the latitudinal gradient of population density, and stable over time. The 2-3-year periodicities were weaker, unstable over time and had spatially fragmented clustering. The rainy season was associated with a lower risk of measles case reporting. The annual periodicity likely reflects seasonal agricultural labour migration, whereas the 2-3-year periodicity potentially results from multiple mechanisms such as reintroductions and vaccine coverage heterogeneity. Our findings suggest that improving vaccine coverage in seasonally mobile populations could reduce strong measles seasonality in Niger and across similar settings.

Coexisting attractors in the context of cross-scale population dynamics: measles in London as a case study

Patterns of measles infection in large urban populations have long been considered the paradigm of synchronized nonlinear dynamics. Indeed, recurrent epidemics appear approximately mass-action despite underlying heterogeneity. However, using a subset of rich, newly digitized mortality data (1897–1906), we challenge that proposition. We find that sub-regions of London exhibited a mixture of simultaneous annual and biennial dynamics, while the aggregate city-level dynamics appears firmly annual. Using a simple stochastic epidemic model and maximum-likelihood inference methods, we show that we can capture this observed variation in periodicity. We identify agreement between theory and data, indicating that both changes in periodicity and epidemic coupling between regions can follow relatively simple rules; in particular we find local variation in seasonality drives periodicity. Our analysis underlines that multiple attractors can coexist in a strongly mixed population and follow theoretical predictions.

Algorithmic discovery of dynamic models from infectious disease data

Theoretical models are typically developed through a deductive process where a researcher formulates a system of dynamic equations from hypothesized mechanisms. Recent advances in algorithmic methods can discover dynamic models inductively-directly from data. Most previous research has tested these methods by rediscovering models from synthetic data generated by the already known model. Here we apply Sparse Identification of Nonlinear Dynamics (SINDy) to discover mechanistic equations for disease dynamics from case notification data for measles, chickenpox, and rubella. The discovered models provide a good qualitative fit to the observed dynamics for all three diseases, However, the SINDy chickenpox model appears to overfit the empirical data, and recovering qualitatively correct rubella dynamics requires using power spectral density in the goodness-of-fit criterion. When SINDy uses a library of second-order functions, the discovered models tend to include mass action incidence and a seasonally varying transmission rate-a common feature of existing epidemiological models for childhood infectious diseases. We also find that the SINDy measles model is capable of out-of-sample prediction of a dynamical regime shift in measles case notification data. These results demonstrate the potential for algorithmic model discovery to enrich scientific understanding by providing a complementary approach to developing theoretical models.