Model-based comprehensive analysis of school closure policies for mitigating influenza epidemics and pandemics

School closure policies are among the non-pharmaceutical measures taken into consideration to mitigate influenza epidemics and pandemics spread. However, a systematic review of the effectiveness of alternative closure policies has yet to emerge. Here we perform a model-based analysis of four types of school closure, ranging from the nationwide closure of all schools at the same time to reactive gradual closure, starting from class-by-class, then grades and finally the whole school. We consider policies based on triggers that are feasible to monitor, such as school absenteeism and national ILI surveillance system. We found that, under specific constraints on the average number of weeks lost per student, reactive school-by-school, gradual, and county-wide closure give comparable outcomes in terms of optimal infection attack rate reduction, peak incidence reduction or peak delay. Optimal implementations generally require short closures of one week each; this duration is long enough to break the transmission chain without leading to unnecessarily long periods of class interruption. Moreover, we found that gradual and county closures may be slightly more easily applicable in practice as they are less sensitive to the value of the excess absenteeism threshold triggering the start of the intervention. These findings suggest that policy makers could consider school closure policies more diffusely as response strategy to influenza epidemics and pandemics, and the fact that some countries already have some experience of gradual or regional closures for seasonal influenza outbreaks demonstrates that logistic and feasibility challenges of school closure strategies can be to some extent overcome

Estimating measles transmission potential in Italy over the period 2010-2011

Background. Recent history of measles epidemiology in Italy is characterized by the recurrence of spatially localized epidemics. Aim. In this study we investigate the three major outbreaks occurred in Italy over the period 2010-2011 and estimate the measles transmission potential. The epidemics mainly involved individuals aged 10-28 years and the transmission potential, measured as effective reproduction number – i.e. the number of new infections generated by a primary infector – was estimated to be 1.9-5.9.Results. Despite such high values, we found that, in all investigated outbreaks, the reproduction number has remained above the epidemic threshold for no more than twelve weeks, suggesting that measles may hardly have the potential to give rise to new nationwide epidemics.Conclusion. In conclusion, the performed analysis highlights the need of planning additional vaccination programs targeting those age classes currently showing a higher susceptibility to infection, in order not to compromise the elimination goal by 201

Real-time numerical forecast of global epidemic spreading: Case study of 2009 A/H1N1pdm

Background Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches. Methods We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability. Results Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model. Conclusions Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high-performance computing. The quality of the forecast depends on the level of data integration, thus stressing the need for high-quality data in population-based models, and of progressive updates of validated available empirical knowledge to inform these models

Ethylene production affects blueberry fruit texture and storability

Ethylene, produced endogenously by plants and their organs, can induce a wide array of physiological responses even at very low concentrations. Nevertheless, the role of ethylene in regulating blueberry (Vaccinium spp.) ripening and storability is still unclear although an increase in ethylene production has been observed in several studies during blueberry ripening. To overcome this issue, we evaluated the endogenous ethylene production of a Vaccinium germplasm selection at different fruit ripening stages and after cold storage, considering also textural modifications. Ethylene and texture were further assessed also on a bi-parental full-sib population of 124 accessions obtained by the crossing between "Draper" and "Biloxi", two cultivars characterized by a different chilling requirement and storability performances. Our results were compared with an extensive literature research, carried out to collect all accessible information on published works related to Vaccinium ethylene production and sensitivity. Results of this study illustrate a likely role of ethylene in regulating blueberry shelf life. However, a generalisation valid for all Vaccinium species is not attainable because of the high variability in ethylene production between genotypes, which is strictly genotype-specific. These differences in ethylene production are related with blueberry fruit storage performances based on textural alterations. Specifically, blueberry accessions characterized by the highest ethylene production had a more severe texture decay during storage. Our results support the possibility of tailoring ad hoc preharvest and postharvest strategies to extend blueberry shelf life and quality according with the endogenous ethylene production level of each cultivar

Ester content of blueberry fruit can be ruled by tailored controlled atmosphere storage management

This study examines the effects of controlled atmosphere (CA) storage with high CO2 concentration (16 KPa) on the volatile organic compound (VOC) profile of blueberries (Vaccinium spp.), considering their genetic variability. The research focuses on the denovo production of esters and their association with fermentation related VOCs, employing complementary analytical techniques for comprehensive VOC profiling: direct injection mass spectrometry using Proton Transfer Reaction - Time of Flight - Mass Spectrometry (PTR-ToF-MS) and gas chromatography utilizing Solid-phase microextraction coupled to gas chromatography-mass spectrometry (SPME/GC-MS). In the first experiment, PTR-ToF-MS and SPME/GC-MS were applied to analyze the volatilome of seven blueberry cultivars under regular (RA) and controlled (CA) atmosphere storage conditions for 42 days. In the second experiment, 39 cultivars were tested to evaluate genetic variability in response to CA storage using PTR-ToF-MS. The third experiment focused on the effect of different oxygen concentrations during storage (1, 7, and 12 kPa O2), studying four cultivars using PTR-ToF-MS. Results of the three experiments revealed high variability among Vaccinium genotypes for all quality traits, which was amplified during storage, particularly under modified atmosphere conditions. CA storage generally enhanced the positive effects of cold storage by reducing texture decay and water loss and improving VOC profiles. Several ester compounds were synthesized de novo under low oxygen conditions, possibly as a response to hypoxic stress. The study concludes that CA storage offers potential to enhance postharvest fruit quality beyond shelf-life extension. The increase in fruity ester compounds during storage may improve blueberries' organoleptic properties. However, the variability in responses among cultivars needs tailored storage protocols. This research provides valuable insights for market segmentation and breeding programs aimed at enhancing blueberry quality and storability, while also validating PTR-ToF-MS as a rapid phenotyping tool for blueberry assessmen

The 2014 Ebola virus disease outbreak in Pujehun, Sierra Leone: epidemiology and impact of interventions

BACKGROUND: In July 2014, an outbreak of Ebola virus disease (EVD) started in Pujehun district, Sierra Leone. On January 10th, 2015, the district was the first to be declared Ebola-free by local authorities after 49 cases and a case fatality rate of 85.7 %. The Pujehun outbreak represents a precious opportunity for improving the body of work on the transmission characteristics and effects of control interventions during the 2014–2015 EVD epidemic in West Africa. METHODS: By integrating hospital registers and contact tracing form data with healthcare worker and local population interviews, we reconstructed the transmission chain and investigated the key time periods of EVD transmission. The impact of intervention measures has been assessed using a microsimulation transmission model calibrated with the collected data. RESULTS: The mean incubation period was 9.7 days (range, 6–15). Hospitalization rate was 89 %. The mean time from the onset of symptoms to hospitalization was 4.5 days (range, 1–9). The mean serial interval was 13.7 days (range, 2–18). The distribution of the number of secondary cases (R(0) = 1.63) was well fitted by a negative binomial distribution with dispersion parameter k = 0.45 (95 % CI, 0.19–1.32). Overall, 74.3 % of transmission events occurred between members of the same family or extended family, 17.9 % in the community, mainly between friends, and 7.7 % in hospital. The mean number of contacts investigated per EVD case raised from 11.5 in July to 25 in September 2014. In total, 43.0 % of cases were detected through contact investigation. Model simulations suggest that the most important factors determining the probability of disease elimination are the number of EVD beds, the mean time from symptom onset to isolation, and the mean number of contacts traced per case. By assuming levels and timing of interventions performed in Pujehun, the estimated probability of eliminating an otherwise large EVD outbreak is close to 100 %. CONCLUSIONS: Containment of EVD in Pujehun district is ascribable to both the natural history of the disease (mainly transmitted through physical contacts, long generation time, overdispersed distribution of secondary cases per single primary case) and intervention measures (isolation of cases and contact tracing), which in turn strongly depend on preparedness, population awareness, and compliance. Our findings are also essential to determine a successful ring vaccination strategy. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12916-015-0524-z) contains supplementary material, which is available to authorized users

Evaluating vaccination strategies for reducing infant respiratory syncytial virus infection in low-income settings

Background: Respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract disease and related hospitalization of young children in least developed countries. Individuals are repeatedly infected, but it is the first exposure, often in early infancy, that results in the vast majority of severe RSV disease. Unfortunately, due to immunological immaturity, infants are a problematic RSV vaccine target. Several trials are ongoing to identify a suitable candidate vaccine and target group, but no immunization program is yet in place. Methods: In this work, an individual-based model that explicitly accounts for the socio-demographic population structure is developed to investigate RSV transmission patterns in a rural setting of Kenya and to evaluate the potential effectiveness of alternative population targets in reducing RSV infant infection. Results: We find that household transmission is responsible for 39% of infant infections and that school-age children are the main source of infection within the household, causing around 55% of cases. Moreover, assuming a vaccine-induced protection equivalent to that of natural infection, our results show that annual vaccination of students is the only alternative strategy to routine immunization of infants able to trigger a relevant and persistent reduction of infant infection (on average, of 35.6% versus 41.5% in 10 years of vaccination). Interestingly, if vaccination of pregnant women boosts maternal antibody protection in infants by an additional 4 months, RSV infant infection will be reduced by 31.5%. Conclusions: These preliminary evaluations support the efforts to develop vaccines and related strategies that go beyond targeting vaccines to those at highest risk of severe disease

A multiscale modeling framework for Scenario Modeling: Characterizing the heterogeneity of the COVID-19 epidemic in the US.

The Scenario Modeling Hub (SMH) initiative provides projections of potential epidemic scenarios in the United States (US) by using a multi-model approach. Our contribution to the SMH is generated by a multiscale model that combines the global epidemic metapopulation modeling approach (GLEAM) with a local epidemic and mobility model of the US (LEAM-US), first introduced here. The LEAM-US model consists of 3142 subpopulations each representing a single county across the 50 US states and the District of Columbia, enabling us to project state and national trajectories of COVID-19 cases, hospitalizations, and deaths under different epidemic scenarios. The model is age-structured, and multi-strain. It integrates data on vaccine administration, human mobility, and non-pharmaceutical interventions. The model contributed to all 17 rounds of the SMH, and allows for the mechanistic characterization of the spatio-temporal heterogeneities observed during the COVID-19 pandemic. Here we describe the mathematical and computational structure of our model, and present the results concerning the emergence of the SARS-CoV-2 Alpha variant (lineage designation B.1.1.7) as a case study. Our findings show considerable spatial and temporal heterogeneity in the introduction and diffusion of the Alpha variant, both at the level of individual states and combined statistical areas, as it competes against the ancestral lineage. We discuss the key factors driving the time required for the Alpha variant to rise to dominance within a population, and quantify the impact that the emergence of the Alpha variant had on the effective reproduction number at the state level. Overall, we show that our multiscale modeling approach is able to capture the complexity and heterogeneity of the COVID-19 pandemic response in the US

Characterizing the transmission patterns of seasonal influenza in Italy: lessons from the last decade

Background: Despite thousands of influenza cases annually recorded by surveillance systems around the globe, estimating the transmission patterns of seasonal influenza is challenging. Methods: We develop an age-structured mathematical model to influenza transmission to analyze ten consecutive seasons (from 2010 to 2011 to 2019–2020) of influenza epidemiological and virological data reported to the Italian surveillance system. Results: We estimate that 18.4–29.3% of influenza infections are detected by the surveillance system. Influenza infection attack rate varied between 12.7 and 30.5% and is generally larger for seasons characterized by the circulation of A/H3N2 and/or B types/subtypes. Individuals aged 14 years or less are the most affected age-segment of the population, with A viruses especially affecting children aged 0–4 years. For all influenza types/subtypes, the mean effective reproduction number is estimated to be generally in the range 1.09–1.33 (9 out of 10 seasons) and never exceeding 1.41. The age-specific susceptibility to infection appears to be a type/subtype-specific feature. Conclusions: The results presented in this study provide insights on type/subtype-specific transmission patterns of seasonal influenza that could be instrumental to fine-tune immunization strategies and non-pharmaceutical interventions aimed at limiting seasonal influenza spread and burden