Mathematical models for dengue fever epidemiology: A 10-year systematic review.

Mathematical models have a long history in epidemiological research, and as the COVID-19 pandemic progressed, research on mathematical modeling became imperative and very influential to understand the epidemiological dynamics of disease spreading. Mathematical models describing dengue fever epidemiological dynamics are found back from 1970. Dengue fever is a viral mosquito-borne infection caused by four antigenically related but distinct serotypes (DENV-1 to DENV-4). With 2.5 billion people at risk of acquiring the infection, it is a major international public health concern. Although most of the cases are asymptomatic or mild, the disease immunological response is complex, with severe disease linked to the antibody-dependent enhancement (ADE) - a disease augmentation phenomenon where pre-existing antibodies to previous dengue infection do not neutralize but rather enhance the new infection. Here, we present a 10-year systematic review on mathematical models for dengue fever epidemiology. Specifically, we review multi-strain frameworks describing host-to-host and vector-host transmission models and within-host models describing viral replication and the respective immune response. Following a detailed literature search in standard scientific databases, different mathematical models in terms of their scope, analytical approach and structural form, including model validation and parameter estimation using empirical data, are described and analyzed. Aiming to identify a consensus on infectious diseases modeling aspects that can contribute to public health authorities for disease control, we revise the current understanding of epidemiological and immunological factors influencing the transmission dynamics of dengue. This review provide insights on general features to be considered to model aspects of real-world public health problems, such as the current epidemiological scenario we are living in.

A multiscale network-based model of contagion dynamics: Heterogeneity, spatial distancing and vaccination

Lockdown and vaccination policies have been the major concern in the last year in order to contain the SARS-CoV-2 infection during the COVID-19 pandemic. In this paper, we present a model able to evaluate alternative lockdown policies and vaccination strategies. Our approach integrates and refines the multiscale model proposed by Bellomo et al., 2020, analyzing alternative network structures and bridging two perspectives to study complexity of living systems. Inside different matrices of contacts we explore the impact of closures of distinct nodes upon the overall contagion dynamics. Social distancing is shown to be more effective when targeting the reduction of contacts among and inside the most vulnerable nodes, namely hospitals/nursing homes. Moreover, our results suggest that school closures alone would not significantly affect the infection dynamics and the number of deaths in the population. Finally, we investigate a scenario with immunization in order to understand the effectiveness of targeted vaccination policies towards the most vulnerable individuals. Our model agrees with the current proposed vaccination strategy prioritizing the most vulnerable segment of the population to reduce severe cases and deaths. [ABSTRACT FROM AUTHOR] Copyright of Mathematical Models & Methods in Applied Sciences is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Critical fluctuations in epidemic models explain COVID-19 post-lockdown dynamics.

As the COVID-19 pandemic progressed, research on mathematical modeling became imperative and very influential to understand the epidemiological dynamics of disease spreading. The momentary reproduction ratio r(t) of an epidemic is used as a public health guiding tool to evaluate the course of the epidemic, with the evolution of r(t) being the reasoning behind tightening and relaxing control measures over time. Here we investigate critical fluctuations around the epidemiological threshold, resembling new waves, even when the community disease transmission rate [Formula: see text] is not significantly changing. Without loss of generality, we use simple models that can be treated analytically and results are applied to more complex models describing COVID-19 epidemics. Our analysis shows that, rather than the supercritical regime (infectivity larger than a critical value, [Formula: see text]) leading to new exponential growth of infection, the subcritical regime (infectivity smaller than a critical value, [Formula: see text]) with small import is able to explain the dynamic behaviour of COVID-19 spreading after a lockdown lifting, with [Formula: see text] hovering around its threshold value.

A multiscale model of virus pandemic: Heterogeneous interactive entities in a globally connected world.

This paper is devoted to the multidisciplinary modelling of a pandemic initiated by an aggressive virus, specifically the so-called SARS-CoV-2 Severe Acute Respiratory Syndrome, corona virus n.2. The study is developed within a multiscale framework accounting for the interaction of different spatial scales, from the small scale of the virus itself and cells, to the large scale of individuals and further up to the collective behaviour of populations. An interdisciplinary vision is developed thanks to the contributions of epidemiologists, immunologists and economists as well as those of mathematical modellers. The first part of the contents is devoted to understanding the complex features of the system and to the design of a modelling rationale. The modelling approach is treated in the second part of the paper by showing both how the virus propagates into infected individuals, successfully and not successfully recovered, and also the spatial patterns, which are subsequently studied by kinetic and lattice models. The third part reports the contribution of research in the fields of virology, epidemiology, immune competition, and economy focussed also on social behaviours. Finally, a critical analysis is proposed looking ahead to research perspectives.