Modelli epidemiologici semplici e complessi: il caso della meningite.

Il lavoro affronta alcuni problemi legati alla modellizzazione matematica della meninigite meningoccica, il tipo di meningite maggiormente responsabile di epidemie nel mondo. Il primo capitolo presenta una panoramica dei modelli fondamentali delle malattie infettive, individuandone le caratteristiche generali e studiando alcuni problemi specifici relativi all'eterogeneità delle popolazioni; nel secondo capitolo si presenta una sintesi della letteratura epidemiologica sulla meningite meningococcica; il terzo capitolo riporta alcune elaborazioni elementari sui dati di notifica italiani del periodo 1994-2006, con relativa discussione; nel quarto capitolo viene rassegnata criticamente la letteratura matematica relativa al meningococco; questa letteratura sorprendentemente non discute uno dei tratti fondamentali della meningite, ossia un'epidemiologia complessa caratterizzata dalla coesistenza di diversi ceppi patogeni cross-immunizzanti che coesistono a loro volta con ceppi non patogeni ma cross-immunizzanti rispetto ai ceppi patogeni. Pertanto nel quinto capitolo vengono a) discusse criticamente le carenze dei modelli esistenti e b) formulati e studiati alcuni modelli in grado di spiegare i meccanismi che permettono la coesistenza di diversi serogruppi; il sesto capitolo mostra una parametrizzazione realistica del modello più promettente e le relative simulazioni, che indicano come la strutturazione in età di una popolazione può giustificare la coesistenza tra diversi tipi e serogruppi di batteri Neisseria. Simple versus complex epidemiological models: the case of meningitis The present work deals with some aspects of the mathematical modeling of meningococcal meningitis, the most important form of meningitis worldwide. The first chapter presents a review of the fundamental epidemiological models, underlines their general features and studies the problem of population diversity. In the second chapter, we summarize the scientific literature on the epidemiology of meningococcal meningitis. The third chapter shows some elementary analysis and a discussion of the italian notification data from the period 1994-2006. In the fourth chapter we summarize and critically review the mathematical literature on the meningococcus; it is surprising to find that none of the available models discusses one of the most important features of the complex meningococcal epidemiology: the coexistence among different, mutually exclusive bacterial strains, and between Neisseria meningitidis and lactamica. For this reason, in the fifth chapter we: a) critically discuss the existing models of coexistence and b) formulate and analyze some models which are able to explain the mechanisms allowing for serogroup coexistence. The sixth chapter shows a realistic parametrization of the most promising model and the relative dynamical simulations, which show that age-structured population may justify the coexistence among different types and serogroups of the bacteria of the genus Neisseria

Transmission dynamics of the ongoing chikungunya outbreak in Central Italy. From coastal areas to the metropolitan city of Rome, summer 2017

A large chikungunya outbreak is ongoing in Italy, with a main cluster in the Anzio coastal municipality. With preliminary epidemiological data, and a transmission model using mosquito abundance and biting rates, we estimated the basic reproduction number R0 at 2.07 (95% credible interval: 1.47–2.59) and the first case importation between 21 May and 18 June 2017. Outbreak risk was higher in coastal/rural sites than urban ones. Novel transmission foci could occur up to mid-November

Investigating the relationship between interventions, contact patterns, and SARS-CoV-2 transmissibility

Background: After a rapid upsurge of COVID-19 cases in Italy during the fall of 2020, the government introduced a three-tiered restriction system aimed at increasing physical distancing. The Ministry of Health, after periodic epidemiological risk assessments, assigned a tier to each of the 21 Italian regions and autonomous provinces. It is still unclear to what extent these different sets of measures altered the number of daily interactions and the social mixing patterns.Methods and findings: We conducted a survey between July 2020 and March 2021 to monitor changes in social contact patterns among individuals in the metropolitan city of Milan, Italy, which was hardly hit by the second wave of the COVID-19 pandemic. The number of daily contacts during periods characterized by different levels of restrictions was analyzed through negative binomial regression models and age-specific contact matrices were estimated under the different tiers of restrictions. By relying on the empirically estimated mixing patterns, we quantified relative changes in SARS-CoV-2 transmission potential associated with the different tiers.As tighter restrictions were implemented during the fall of 2020, a progressive reduction in the mean number of daily contacts recorded by study participants was observed: from 15.9 % under mild restrictions (yellow tier), to 41.8 % under strong restrictions (red tier). Higher restrictions levels were also found to increase the relative contribution of contacts occurring within the household. The SARS-CoV-2 reproduction number was estimated to decrease by 17.1 % (95 %CI: 1.5-30.1), 25.1 % (95 %CI: 13.0-36.0) and 44.7 % (95 %CI: 33.9-53.0) under the yellow, orange, and red tiers, respectively.Conclusions: Our results give an important quantification of the expected contribution of different restriction levels in shaping social contacts and decreasing the transmission potential of SARS-CoV-2. These estimates can find an operational use in anticipating the effect that the implementation of these tiered restriction can have on SARS-CoV-2 reproduction number under an evolving epidemiological situation

A quantitative assessment of epidemiological parameters required to investigate COVID-19 burden

Solid estimates describing the clinical course of SARS-CoV-2 infections are still lacking due to under-ascertainment of asymptomatic and mild-disease cases. In this work, we quantify age-specific probabilities of transitions between stages defining the natural history of SARS-CoV-2 infection from 1965 SARS-CoV-2 positive individuals identified in Italy between March and April 2020 among contacts of confirmed cases. Infected contacts of cases were confirmed via RT-PCR tests as part of contact tracing activities or retrospectively via IgG serological tests and followed-up for symptoms and clinical outcomes. In addition, we provide estimates of time intervals between key events defining the clinical progression of cases as obtained from a larger sample, consisting of 95,371 infections ascertained between February and July 2020. We found that being older than 60 years of age was associated with a 39.9% (95%CI: 36.2–43.6%) likelihood of developing respiratory symptoms or fever ≥ 37.5 °C after SARS-CoV-2 infection; the 22.3% (95%CI: 19.3–25.6%) of the infections in this age group required hospital care and the 1% (95%CI: 0.4–2.1%) were admitted to an intensive care unit (ICU). The corresponding proportions in individuals younger than 60 years were estimated at 27.9% (95%CI: 25.4–30.4%), 8.8% (95%CI: 7.3–10.5%) and 0.4% (95%CI: 0.1–0.9%), respectively. The infection fatality ratio (IFR) ranged from 0.2% (95%CI: 0.0–0.6%) in individuals younger than 60 years to 12.3% (95%CI: 6.9–19.7%) for those aged 80 years or more; the case fatality ratio (CFR) in these two age classes was 0.6% (95%CI: 0.1–2%) and 19.2% (95%CI: 10.9–30.1%), respectively. The median length of stay in hospital was 10 (IQR: 3–21) days; the length of stay in ICU was 11 (IQR: 6–19) days. The obtained estimates provide insights into the epidemiology of COVID-19 and could be instrumental to refine mathematical modeling work supporting public health decisions

household transmission and disease transmissibility of a large hav outbreak in lazio italy 2016 2017

Abstract A major outbreak of Hepatitis A Virus (HAV) has swept through Europe between mid-2016 and 2017, mainly within the community of men who have sex with men (MSM). Over the same period, about 1,000 outbreak-related cases of acute Hepatitis A (AHA) were recorded in Lazio region, Italy. We calibrated a Bayesian model to reconstruct likely transmission events within all 44 households where multiple infections were recorded, representing a total of 103 cases from the HAV outbreak in Lazio. Based on information on the observed times of symptom onset, we estimated for the probability distribution function of the HAV generation time and used it to compute the effective and instantaneous reproduction numbers for the considered outbreak from the overall epidemic curve (N = 998 cases). We estimated a mean generation time of 30.2 days (95%CI: 25.2-33.0) and an effective reproduction number of about 1.63 (95% CI: 1.35-1.94). Transmissibility peaked in January 2017, shortly before targeted awareness and vaccination campaigns were put in place by health authorities; however, transmission remained above the epidemic threshold until June 2017. Within households, children (0-15) and young adults (16-30) infected preferentially individuals of the same age class, whereas transmission within older age groups was substantially homogeneous. These results suggest that the implemented interventions were able to slow down HAV transmission, but not to bring it rapidly to a halt. According to our estimates of the HAV transmissibility, about 50% of the at-risk persons should be immunized to prevent similar outbreaks in the future. Our results also indicate spillover from community transmission to household members, suggesting the opportunity of vaccinating household contacts of cases to prevent further spread of the epidemics

Estimating SARS-CoV-2 transmission in educational settings: a retrospective cohort study

Background School closures and distance learning have been extensively adopted to counter the COVID-19 pandemic. However, the contribution of school transmission to the spread of SARS-CoV-2 remains poorly quantified. Methods We analyzed transmission patterns associated with 976 SARS-CoV-2 exposure events, involving 460 positive individuals, as identified in early 2021 through routine surveillance and an extensive screening conducted on students, school personnel, and their household members in a small Italian municipality. In addition to population screenings and contact-tracing operations, reactive closures of class and schools were implemented. Results From the analysis of 152 clear infection episodes and 584 exposure events identified by epidemiological investigations, we estimated that approximately 50%, 21%, and 29% of SARS-CoV-2 transmission was associated with household, school, and community contacts, respectively. We found substantial transmission heterogeneities, with 20% positive individuals causing 75% to 80% of ascertained infection episodes. A higher proportion of infected individuals causing onward transmission was found among students (46.2% vs. 25%, on average), who also caused a markedly higher number of secondary cases (mean: 1.03 vs. 0.35). By reconstructing likely transmission chains from the entire set of exposures identified during contact-tracing operations, we found that clusters originated from students or school personnel were associated with a larger average cluster size (3.32 vs. 1.15) and a larger average number of generations in the transmission chain (1.56 vs. 1.17). Conclusions Uncontrolled SARS-CoV-2 transmission at school could disrupt the regular conduct of teaching activities, likely seeding the transmission into other settings, and increasing the burden on contact-tracing operations

Estimating SARS-CoV-2 transmission in educational settings: a retrospective cohort study

Background School closures and distance learning have been extensively adopted to counter the COVID-19 pandemic. However, the contribution of school transmission to the spread of SARS-CoV-2 remains poorly quantified. Methods We analyzed transmission patterns associated with 976 SARS-CoV-2 exposure events, involving 460 positive individuals, as identified in early 2021 through routine surveillance and an extensive screening conducted on students, school personnel, and their household members in a small Italian municipality. In addition to population screenings and contact-tracing operations, reactive closures of class and schools were implemented. Results From the analysis of 152 clear infection episodes and 584 exposure events identified by epidemiological investigations, we estimated that approximately 50%, 21%, and 29% of SARS-CoV-2 transmission was associated with household, school, and community contacts, respectively. We found substantial transmission heterogeneities, with 20% positive individuals causing 75% to 80% of ascertained infection episodes. A higher proportion of infected individuals causing onward transmission was found among students (46.2% vs. 25%, on average), who also caused a markedly higher number of secondary cases (mean: 1.03 vs. 0.35). By reconstructing likely transmission chains from the entire set of exposures identified during contact-tracing operations, we found that clusters originated from students or school personnel were associated with a larger average cluster size (3.32 vs. 1.15) and a larger average number of generations in the transmission chain (1.56 vs. 1.17). Conclusions Uncontrolled SARS-CoV-2 transmission at school could disrupt the regular conduct of teaching activities, likely seeding the transmission into other settings, and increasing the burden on contact-tracing operations

Infection fatality ratio of SARS-CoV-2 in Italy

We analyzed 5,484 close contacts of COVID-19 cases from Italy, all of them tested for SARS-CoV-2 infection. We found an infection fatality ratio of 2.2% (95%CI 1.69-2.81%) and identified male sex, age >70 years, cardiovascular comorbidities, and infection early in the epidemics as risk factors for death

Co-circulation of SARS-CoV-2 Alpha and Gamma variants in Italy, February and March 2021

: BackgroundSeveral SARS-CoV-2 variants of concern (VOC) have emerged through 2020 and 2021. There is need for tools to estimate the relative transmissibility of emerging variants of SARS-CoV-2 with respect to circulating strains.AimWe aimed to assess the prevalence of co-circulating VOC in Italy and estimate their relative transmissibility.MethodsWe conducted two genomic surveillance surveys on 18 February and 18 March 2021 across the whole Italian territory covering 3,243 clinical samples and developed a mathematical model that describes the dynamics of co-circulating strains.ResultsThe Alpha variant was already dominant on 18 February in a majority of regions/autonomous provinces (national prevalence: 54%) and almost completely replaced historical lineages by 18 March (dominant across Italy, national prevalence: 86%). We found a substantial proportion of the Gamma variant on 18 February, almost exclusively in central Italy (prevalence: 19%), which remained similar on 18 March. Nationally, the mean relative transmissibility of Alpha ranged at 1.55-1.57 times the level of historical lineages (95% CrI: 1.45-1.66). The relative transmissibility of Gamma varied according to the assumed degree of cross-protection from infection with other lineages and ranged from 1.12 (95% CrI: 1.03-1.23) with complete immune evasion to 1.39 (95% CrI: 1.26-1.56) for complete cross-protection.ConclusionWe assessed the relative advantage of competing viral strains, using a mathematical model assuming different degrees of cross-protection. We found substantial co-circulation of Alpha and Gamma in Italy. Gamma was not able to outcompete Alpha, probably because of its lower transmissibility