The coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)1. Here we introduce a metapopulation susceptible-exposed-infectious-removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of 'superspreader' points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2-8 solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19.

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Mobility network models of COVID-19 explain inequities and inform reopening.

The spread of coronavirus disease 2019 (COVID-19) in Italy prompted drastic measures for transmission containment. We examine the effects of these interventions, based on modeling of the unfolding epidemic. We test modeling options of the spatially explicit type, suggested by the wave of infections spreading from the initial foci to the rest of Italy. We estimate parameters of a metacommunity Susceptible-Exposed-Infected-Recovered (SEIR)-like transmission model that includes a network of 107 provinces connected by mobility at high resolution, and the critical contribution of presymptomatic and asymptomatic transmission. We estimate a generalized reproduction number ([Formula: see text] = 3.60 [3.49 to 3.84]), the spectral radius of a suitable next-generation matrix that measures the potential spread in the absence of containment interventions. The model includes the implementation of progressive restrictions after the first case confirmed in Italy (February 21, 2020) and runs until March 25, 2020. We account for uncertainty in epidemiological reporting, and time dependence of human mobility matrices and awareness-dependent exposure probabilities. We draw scenarios of different containment measures and their impact. Results suggest that the sequence of restrictions posed to mobility and human-to-human interactions have reduced transmission by 45% (42 to 49%). Averted hospitalizations are measured by running scenarios obtained by selectively relaxing the imposed restrictions and total about 200,000 individuals (as of March 25, 2020). Although a number of assumptions need to be reexamined, like age structure in social mixing patterns and in the distribution of mobility, hospitalization, and fatality, we conclude that verifiable evidence exists to support the planning of emergency measures.

/pdf/spread-and-dynamics-of-the-covid-19-epidemic-in-italy-2rtidiwkfq.pdf

Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures.

The COVID-19 pandemic has created a public health crisis. Because SARS-CoV-2 can spread from individuals with presymptomatic, symptomatic, and asymptomatic infections, the reopening of societies and the control of virus spread will be facilitated by robust population screening, for which virus testing will often be central. After infection, individuals undergo a period of incubation during which viral titers are too low to detect, followed by exponential viral growth, leading to peak viral load and infectiousness and ending with declining titers and clearance. Given the pattern of viral load kinetics, we model the effectiveness of repeated population screening considering test sensitivities, frequency, and sample-to-answer reporting time. These results demonstrate that effective screening depends largely on frequency of testing and speed of reporting and is only marginally improved by high test sensitivity. We therefore conclude that screening should prioritize accessibility, frequency, and sample-to-answer time; analytical limits of detection should be secondary.

Test sensitivity is secondary to frequency and turnaround time for COVID-19 screening.

Many public health responses and modeled scenarios for COVID-19 outbreaks caused by SARS-CoV-2 assume that infection results in an immune response that protects individuals from future infections or illness for some amount of time. The presence or absence of protective immunity due to infection or vaccination (when available) will affect future transmission and illness severity. Here, we review the scientific literature on antibody immunity to coronaviruses, including SARS-CoV-2 as well as the related SARS-CoV, MERS-CoV and endemic human coronaviruses (HCoVs). We reviewed 2,452 abstracts and identified 491 manuscripts relevant to 5 areas of focus: 1) antibody kinetics, 2) correlates of protection, 3) immunopathogenesis, 4) antigenic diversity and cross-reactivity, and 5) population seroprevalence. While further studies of SARS-CoV-2 are necessary to determine immune responses, evidence from other coronaviruses can provide clues and guide future research.

/pdf/a-systematic-review-of-antibody-mediated-immunity-to-2kcgg1pgct.pdf

A systematic review of antibody mediated immunity to coronaviruses: kinetics, correlates of protection, and association with severity.

The COVID-19 pandemic has shown a markedly low proportion of cases among children. Age disparities in observed cases could be explained by assortative mixing patterns and reactive school closures which decrease mixing between children, or by children exhibiting lower susceptibility to infection, or by children having a lower propensity to show clinical symptoms. We formally test these hypotheses by fitting an age-structured mathematical model to epidemic data from six countries, finding strong age dependence in the probability of developing clinical symptoms, rising from around 20% in under 10s to over 70% in older adults. We find that interventions aimed at halting transmission in children may have minimal effects on preventing cases depending on the relative transmissibility of subclinical infections. Our estimated age-specific clinical fraction has implications for the expected global burden of clinical cases because of demographic differences across settings. In younger populations, the expected clinical attack rate would be lower, although it is likely that comorbidities in low-income countries will affect disease severity. Without effective control measures, regions with older populations may see disproportionally more clinical cases, particularly in the later stages of the pandemic.

/pdf/age-dependent-effects-in-the-transmission-and-control-of-4to34orrpb.pdf

Age-dependent effects in the transmission and control of COVID-19 epidemics

The paper deals with two exact mathematical models of study: A borrowed model with constant intensity of study and amodel with variable intensity of study derived by the authors are presented using data acquired in real pedagogical research. Thebounding of study performance is proved and precisely determined in dependence on personal (group) parameters of study andintensity of forgetting. The parameter of forgetting has decisive influence on total performance.

Mathematical Modelling of Study

Local landscape is a part of the real world that surrounds a person from an early childhood. It offers opportunities for their cognitive development, stimuli for emotional development, shapes their views. A positive relationship with the local region should be developed in Geography education as one of the affective educational goals. The aim of this paper is to evaluate pupils' perception of the concept of a local region and to find out their relationship to their local region based on the results of a questionnaire survey of pupils of lower secondary education in the Czechia and Slovakia. The data were collected using an online questionnaire, which was completed by 4 673 pupils in the Czechia and 3 980 pupils in Slovakia. The results show that pupils perceive their local region most often as the whole district or the whole region in which they live. Pupils' attitude to the local region decreases with their age and was stronger among boys. Slovak pupils identify with their region more and there are significant differences in their relationship to the region across regions. In particular, pupils are most proud of the natural environment and cultural monuments. The findings are beneficial in the field of regional identity research, but also in educational settings, as the local region and a deeper knowledge of it is not only the goal, but also the means of Geographic education.

Ivana Tomcikova

Papers

Mathematical Modelling of Study

Vzťah žiakov k svojmu miestnemu regiónu = Pupils' relationship to their local region regulation