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 better understanding of air quality resulting from the effects of the 2020 pandemic in a city in the equatorial region (Fortaleza, Brazil).

The year 2020 was atypical due to the pandemic caused by the SARS-CoV-2 virus (COVID-19), providing a unique opportunity to understand changes in air quality due to the reduction in urban activity. Therefore, the aim of the present study was to perform an integrated evaluation on the influence of the effects of the 2020 pandemic on air quality in the city of Fortaleza, investigating levels of PM2.5, PM10, NO2, NO, SO2, CO, and O3, corresponding health risks, as well as the influence of meteorological variables and urban activity. In all phases analyzed, significant reductions were found in NOx, NO, NO2, and CO. A considerable reduction in PM2.5 and PM10 was found in the early phases, with an increase in the later phases. These findings are explained by the nearly 50% reduction in vehicular traffic and the consequent reduction in fossil fuel emissions, mainly in the partial lockdown and total lockdown periods, as well as reductions in commercial (stores/shops) and industrial activities. The variation in O3 was initially non-significant, followed by a considerable increase in the last three phases analyzed; this increase was influenced by changes in temperature and the incidence of sunlight. SO2 concentrations increased in the period studied, demonstrating that the vehicular fleet, local commerce, and other activities are not the predominant sources of this compound. Estimated health risks were reduced by half during the lockdown period, especially for non-smokers, followed by a drastic increase in the last three phases. The planetary boundary layer was positively correlated with O3 and PM10 and negatively correlated with NOx, NO2, and NO, indicating its influence on the distribution of pollutants in the lower atmosphere and, consequently, air quality.

The Italian Emergency Regime at the Covid-19 “Stress Test”: Decline of Political Responsiveness, Output Legitimation and Politicization of Expertise

During the Covid-19 pandemic, public trust necessarily shifted towards science and technical expertise worldwide. In some liberal democracies, the Constitution and Parliament have been bypassed, with Executives using scientific and technical expertise to legitimate political choices within the crisis management process. In Italy (March-August 2020), the Executive set up expert teams (such as the Comitato Tecnico-Scientifico) acting mostly by Decrees of the President of Council of Ministers (DPCM). The Italian Parliament was not sufficiently consulted. After reviewing the current research literature on constitutional changes during emergency regimes within representative democracies, and using insights from Italy, we try to frame the discourse concerning Executive's choices during emergency regimes in terms of (i) decline of political responsiveness, (ii) prevalence of output legitimation and (iii) politicization of expertise (with the possibility for expertise, in turn, to influence policy making) to contribute to the overall debate on the reconfiguration of powers in times of crises. © 2021 Author. All Rights Reserved.

Health, Transport and the Environment: The Impacts of the COVID-19 Lockdown on Air Pollution

Lockdown measures were initiated in Italy on March 9th after the start of the SARS-CoV-2 epidemic to flatten the epidemic curve. The aim of the present study was to assess the impact of restrictive measures in the Apulia Region, southern Italy, on air quality from March to April 2020. We applied a dual-track approach. We assessed citizen mobility and vehicle traffic with mobility network data and information obtained from satellite tracking, and we evaluated and compared pollutant concentration data as measured by monitoring stations maintained by the Regional Agency for Environmental Protection and Prevention of Apulia (ARPA). The results showed a decrease in the weekly mean NO₂ concentration recorded by urban traffic stations during the lockdown period. In particular, in the city of Bari, the average NO₂ concentration decreased from 62.2 mug/m³ in March 2019 to 48.2 mug/m³ in March 2020. Regarding PM10 levels, the average concentrations at the individual traffic stations showed no particular variation compared to those in the same months of the previous year, except for Bari-Caldarola Station in March 2019/2020 (p-value < 0.001) and in April 2019/2020 (p-value = 0.04). In particular the average in March 2019 was ~26.9 mug/m³, while that in March 2020 was ~22.9 mug/m³. For April, the average concentration of PM10 in 2019 was 27.9 mug/m³, while in 2020, the average was ~22.4 mug/m³. This can be explained by the fact that PM10 levels are influenced by multiple variables such as weather and climate conditions and desert dust advections.