A Model for Reinfections and the Transition of Epidemics.

Reinfections of infected individuals during a viral epidemic contribute to the continuation of the infection for longer periods of time. In an epidemic, contagion starts with an infection wave, initially growing exponentially fast until it reaches a maximum number of infections, following which it wanes towards an equilibrium state of zero infections, assuming that no new variants have emerged. If reinfections are allowed, multiple such infection waves might occur, and the asymptotic equilibrium state is one in which infection rates are not negligible. This paper analyzes such situations by expanding the traditional SIR model to include two new dimensionless parameters, ε and θ, characterizing, respectively, the kinetics of reinfection and a delay time, after which reinfection commences. We find that depending on these parameter values, three different asymptotic regimes develop. For relatively small θ, two of the regimes are asymptotically stable steady states, approached either monotonically, at larger ε (corresponding to a stable node), or as waves of exponentially decaying amplitude and constant frequency, at smaller ε (corresponding to a spiral). For θ values larger than a critical, the asymptotic state is a periodic pattern of constant frequency. However, when ε is sufficiently small, the asymptotic state is a wave. We delineate these regimes and analyze the dependence of the corresponding population fractions (susceptible, infected and recovered) on the two parameters ε and θ and on the reproduction number R0. The results provide insights into the evolution of contagion when reinfection and the waning of immunity are taken into consideration. A related byproduct is the finding that the conventional SIR model is singular at large times, hence the specific quantitative estimate for herd immunity it predicts will likely not materialize.

Different cytokine and chemokine profiles in hospitalized patients with COVID-19 during the first and second outbreaks from Argentina show no association with clinical comorbidities.

Background: COVID-19 severity has been linked to an increased production of inflammatory mediators called "cytokine storm". Available data is mainly restricted to the first international outbreak and reports highly variable results. This study compares demographic and clinical features of patients with COVID-19 from Córdoba, Argentina, during the first two waves of the pandemic and analyzes association between comorbidities and disease outcome with the "cytokine storm", offering added value to the field. Methods: We investigated serum concentration of thirteen soluble mediators, including cytokines and chemokines, in hospitalized patients with moderate and severe COVID-19, without previous rheumatic and autoimmune diseases, from the central region of Argentina during the first and second infection waves. Samples from healthy controls were also assayed. Clinical and biochemical parameters were collected. Results: Comparison between the two first COVID-19 waves in Argentina highlighted that patients recruited during the second wave were younger and showed less concurrent comorbidities than those from the first outbreak. We also recognized particularities in the signatures of systemic cytokines and chemokines in patients from both infection waves. We determined that concurrent pre-existing comorbidities did not have contribution to serum concentration of systemic cytokines and chemokines in COVID-19 patients. We also identified immunological and biochemical parameters associated to inflammation which can be used as prognostic markers. Thus, IL-6 concentration, C reactive protein level and platelet count allowed to discriminate between death and discharge in patients hospitalized with severe COVID-19 only during the first but not the second wave. Conclusions: Our data provide information that deepens our understanding of COVID-19 pathogenesis linking demographic features of a COVID-19 cohort with cytokines and chemokines systemic concentration, presence of comorbidities and different disease outcomes. Altogether, our findings provide information not only at local level by delineating inflammatory/anti-inflammatory response of patients but also at international level addressing the impact of comorbidities and the infection wave in the variability of cytokine and chemokine production upon SARS-CoV-2 infection.

A Bayesian predictive analytics model for improving long range epidemic forecasting during an infection wave.

Following the outbreak of the coronavirus epidemic in early 2020, municipalities, regional governments and policymakers worldwide had to plan their Non-Pharmaceutical Interventions (NPIs) amidst a scenario of great uncertainty. At this early stage of an epidemic, where no vaccine or medical treatment is in sight, algorithmic prediction can become a powerful tool to inform local policymaking. However, when we replicated one prominent epidemiological model to inform health authorities in a region in the south of Brazil, we found that this model relied too heavily on manually predetermined covariates and was too reactive to changes in data trends. Our four proposed models access data of both daily reported deaths and infections as well as take into account missing data (e.g., the under-reporting of cases) more explicitly, with two of the proposed versions also attempting to model the delay in test reporting. We simulated weekly forecasting of deaths from the period from 31/05/2020 until 31/01/2021, with first week data being used as a cold-start to the algorithm, after which we use a lighter variant of the model for faster forecasting. Because our models are significantly more proactive in identifying trend changes, this has improved forecasting, especially in long-range predictions and after the peak of an infection wave, as they were quicker to adapt to scenarios after these peaks in reported deaths. Assuming reported cases were under-reported greatly benefited the model in its stability, and modelling retroactively-added data (due to the "hot" nature of the data used) had a negligible impact on performance.