A Large Neighbourhood Search Metaheuristic for the Contagious Disease Testing Problem.

In late 2019 a new coronavirus disease (COVID-19) emerged, causing a global pandemic within only a few weeks. A crucial factor in the public health response to pandemics is achieving a short turnaround time between a potential case becoming known, specimen collection and availability of a test result. In this article we address a logistics problem that arises in the context of testing potential cases. We assume that specimens can be collected in two ways: either by means of a mobile test-team or by means of a stationary test-team in a test-centre. After the specimens have been collected they must be delivered to a laboratory in order to be analysed. The problem we address aims at deciding how many test-centres to open and where, how many mobile test-teams to use, which suspected cases to assign to a test-centre and which to visit with a mobile test-team, which specimen to assign to which laboratory, and planning the routes of the mobile test-teams. The objective is to minimise the total cost of opening test-centres and routing mobile test-teams. We introduce this new problem, which we call the contagious disease testing problem (CDTP), and present a mixed-integer linear-programming formulation for it. We propose a large neighbourhood search metaheuristic for solving the CDTP and present an extensive computational study to illustrate its performance. Furthermore, we give managerial insights regarding COVID-19 test logistics, derived from problem instances based on real world data.

Supporting COVID-19 policy-making with a predictive epidemiological multi-model warning system.

BACKGROUND: In response to the SARS-CoV-2 pandemic, the Austrian governmental crisis unit commissioned a forecast consortium with regularly projections of case numbers and demand for hospital beds. The goal was to assess how likely Austrian ICUs would become overburdened with COVID-19 patients in the upcoming weeks. METHODS: We consolidated the output of three epidemiological models (ranging from agent-based micro simulation to parsimonious compartmental models) and published weekly short-term forecasts for the number of confirmed cases as well as estimates and upper bounds for the required hospital beds. RESULTS: We report on three key contributions by which our forecasting and reporting system has helped shaping Austria's policy to navigate the crisis, namely (i) when and where case numbers and bed occupancy are expected to peak during multiple waves, (ii) whether to ease or strengthen non-pharmaceutical intervention in response to changing incidences, and (iii) how to provide hospital managers guidance to plan health-care capacities. CONCLUSIONS: Complex mathematical epidemiological models play an important role in guiding governmental responses during pandemic crises, in particular when they are used as a monitoring system to detect epidemiological change points.

During the SARS-CoV-2 pandemic, health authorities make decisions on how and when to implement interventions such as social distancing to avoid overburdening hospitals and other parts of the healthcare system. We combined three mathematical models developed to predict the expected number of confirmed SARS-CoV-2 cases and hospitalizations over the next two weeks. This provides decision-makers and the general public with a combined forecast that is usually more accurate than any of the individual models. Our forecasting system has been used in Austria to decide when to strengthen or ease response measures.

Meteorological factors and non-pharmaceutical interventions explain local differences in the spread of SARS-CoV-2 in Austria.

The drivers behind regional differences of SARS-CoV-2 spread on finer spatio-temporal scales are yet to be fully understood. Here we develop a data-driven modelling approach based on an age-structured compartmental model that compares 116 Austrian regions to a suitably chosen control set of regions to explain variations in local transmission rates through a combination of meteorological factors, non-pharmaceutical interventions and mobility. We find that more than 60% of the observed regional variations can be explained by these factors. Decreasing temperature and humidity, increasing cloudiness, precipitation and the absence of mitigation measures for public events are the strongest drivers for increased virus transmission, leading in combination to a doubling of the transmission rates compared to regions with more favourable weather. We conjecture that regions with little mitigation measures for large events that experience shifts toward unfavourable weather conditions are particularly predisposed as nucleation points for the next seasonal SARS-CoV-2 waves.

Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities-An Agent-Based Modeling Evaluation.

(1) Background: The Austrian supply of COVID-19 vaccine is limited for now. We aim to provide evidence-based guidance to the authorities in order to minimize COVID-19-related hospitalizations and deaths in Austria. (2) Methods: We used a dynamic agent-based population model to compare different vaccination strategies targeted to the elderly (65 ≥ years), middle aged (45-64 years), younger (15-44 years), vulnerable (risk of severe disease due to comorbidities), and healthcare workers (HCW). First, outcomes were optimized for an initially available vaccine batch for 200,000 individuals. Second, stepwise optimization was performed deriving a prioritization sequence for 2.45 million individuals, maximizing the reduction in total hospitalizations and deaths compared to no vaccination. We considered sterilizing and non-sterilizing immunity, assuming a 70% effectiveness. (3) Results: Maximum reduction of hospitalizations and deaths was achieved by starting vaccination with the elderly and vulnerable followed by middle-aged, HCW, and younger individuals. Optimizations for vaccinating 2.45 million individuals yielded the same prioritization and avoided approximately one third of deaths and hospitalizations. Starting vaccination with HCW leads to slightly smaller reductions but maximizes occupational safety. (4) Conclusion: To minimize COVID-19-related hospitalizations and deaths, our study shows that elderly and vulnerable persons should be prioritized for vaccination until further vaccines are available.

Reproduction of patterns in melanocytic proliferations by agent-based simulation and geometric modeling.

Spatio-temporal patterns of melanocytic proliferations observed in vivo are important for diagnosis but the mechanisms that produce them are poorly understood. Here we present an agent-based model for simulating the emergence of the main biologic patterns found in melanocytic proliferations. Our model portrays the extracellular matrix of the dermo-epidermal junction as a two-dimensional manifold and we simulate cellular migration in terms of geometric translations driven by adhesive, repulsive and random forces. Abstracted cellular functions and melanocyte-matrix interactions are modeled as stochastic events. For identification and validation we use visual renderings of simulated cell populations in a horizontal perspective that reproduce growth patterns observed in vivo by sequential dermatoscopy and corresponding vertical views that reproduce the arrangement of melanocytes observed in histopathologic sections. Our results show that a balanced interplay of proliferation and migration produces the typical reticular pattern of nevi, whereas the globular pattern involves additional cellular mechanisms. We further demonstrate that slight variations in the three basic cellular properties proliferation, migration, and adhesion are sufficient to produce a large variety of morphological appearances of nevi. We anticipate our model to be a starting point for the reproduction of more complex scenarios that will help to establish functional connections between abstracted microscopic behavior and macroscopic patterns in all types of melanocytic proliferations including melanoma.

Why should we apply ABM for decision analysis for infectious diseases?-An example for dengue interventions.

For the evaluation of infectious-diseases interventions, the transmissible nature of such diseases plays a central role. Agent-based models (ABM) allow for dynamic transmission modeling but publications are limited. We aim to provide an overview of important characteristics of ABM for decision-analytic modeling of infectious diseases. A case study of dengue epidemics illustrates model characteristics, conceptualization, calibration and model analysis. First, major characteristics of ABM are outlined and discussed based on ISPOR and ISPOR-SMDM Good Practice guidelines. Second, in our case study, we modeled a dengue outbreak in Cebu City (Philippines) to assess the impact interventions to control the relative growth of the mosquito population. Model outcomes include prevalence and incidence of infected persons. The modular ABM simulates persons and mosquitoes over an annual time horizon considering daily time steps. The model was calibrated and validated. ABM is a dynamic, individual-level modeling approach that is capable to reproduce direct and indirect effects of interventions for infectious diseases. The ability to replicate emerging behavior and to include human behavior or the behavior of other agents is a distinguishing modeling characteristic (e.g., compared to Markov models). Modeling behavior may, however, require extensive calibration and validation. The analyzed hypothetical effectiveness of dengue interventions showed that a reduced human-mosquito ratio of 1:2.5 during rainy seasons leads already to a substantial decrease of infected persons. ABM can support decision-analyses for infectious diseases including disease dynamics, emerging behavior, and providing a high level of reusability due to modularity.