Are Modeling Methods in Health Economics Changing?

Health is a complex system since the appearance of illness and sickness has high uncertainty. In this regard, health economics is a complex system that the emergence of health care demand is uncertain. Thus, evaluation of health economics using classical mathematical methods produce insufficient and incorrect outcomes. Therefore, in this work, complex system tools and methods assessing the health economics and the health care market were investigated to illustrate the superiority of the simulation methods. In recent years, researchers tend to use simulation and modelling based applications to assess the health care market instead of classical methods such as cost-effectiveness and cost-utility. Hence, methods like discrete event simulation, system dynamic modelling, and agent-based modelling attract the attention of researchers. Such methods were used in the evaluation of different health care policies and the assessment of the simulation outputs. Moreover, simulation methods were also used in the minimization of health care expenditures and the assessment of health insurance repayment. It was concluded that researchers tend to use simulation methods to evaluate health economics rather than classical approaches

COVID-Town: An Integrated Economic-Epidemiological Agent-Based Model

I develop a novel macroeconomic epidemiological agent-based model to study the impact of the COVID-19 pandemic under varying policy scenarios. Agents differ with regard to their profession, family status and age and interact with other agents at home, work or during leisure activities. The model allows to implement and test actually used or counterfactual policies such as closing schools or the leisure industry explicitly in the model in order to explore their impact on the spread of the virus, and their economic consequences. The model is calibrated with German statistical data on time use, demography, households, firm demography, employment, company profits and wages. I set up a baseline scenario based on the German containment policies and fit the epidemiological parameters of the simulation to the observed German death curve and an estimated infection curve of the first COVID-19 wave. My model suggests that by acting one week later, the death toll of the first wave in Germany would have been 180% higher, whereas it would have been 60% lower, if the policies had been enacted a week earlier. I finally discuss two stylized fiscal policy scenarios: procyclical (zero-deficit) and anticyclical fiscal policy. In the zero-deficit scenario a vicious circle emerges, in which the economic recession spreads from the high-interaction leisure industry to the rest of the economy. Even after eliminating the virus and lifting the restrictions, the economic recovery is incomplete. Anticyclical fiscal policy on the other hand limits the economic losses and allows for a V-shaped recovery, but does not increase the number of deaths. These results suggest that an optimal response to the pandemic aiming at containment or “holding out for a vaccine” combines early introduction of containment measures to keep the number of infected low with expansionary fiscal policy to keep output in lower risk sectors high

Three Essays in Applied Economics

Governments worldwide support their national economies to obtain growth, adequate employment, and price durability. Regulation is a unique approach through which governments control the economy. Governments attempt to maintain and regulate the economy in various ways to guarantee that business fosters the common good. The range of government regulations is enormous and touches all areas of the economy and all features of daily life. Understanding the effect of regulations on the economy is essential since its outcomes can improve government interventions’ efficiency. Applied economics can help measure the effectiveness of government intervention on economic outcomes. This dissertation includes three essays in applied economics and tries to employ economical methods to understand the outcomes of selected government regulations on the economy and environment. The first essay estimates the effect of Iran’s subsidy reduction on industrial workshops’ total factor productivity (TFP). After the model was applied, results show that Iran’s subsidy reduction on energy and food caused TFP to decrease overall for all industrial workshops. For some industries, however, TFP increased. The second essay tries to determine whether there is a connection between weather conditions and the spread of the COVID-19 disease. Finding this connection could help governments make more informed policy decisions and better prepare for the next waves and subsequent pandemics. The outcomes indicate a negative relationship between the number of infected cases and daily minimum temperature in South Korea. Meanwhile, an increase in air pressure, humidity, and daily minimum wind speed was associated with a higher number of infections. This study focused on the COVID-19 pandemic from the perspective of weather conditions, but other essential factors, like mobility, can also affect the COVID-19 pandemic, and can be as critical as weather conditions. In the last study, this dissertation evaluates how the 2017 Tax Cuts and Jobs Act (TCJA) affected small businesses’ default rate on loans. It focuses on small business loans since activity in this segment is a primary goal of policymakers, and shedding light on this area would help improve their policies regarding small businesses. The results show that the TCJA had a positive effect on avoiding defaults by small businesses. This kind of research could also prove helpful for policymakers since with it they can identify the results of their regulations

Analysing the Combined Health, Social and Economic Impacts of the Corovanvirus Pandemic Using Agent-Based Social Simulation

During the COVID-19 crisis there have been many difficult decisions governments and other decision makers had to make. E.g. do we go for a total lock down or keep schools open? How many people and which people should be tested? Although there are many good models from e.g. epidemiologists on the spread of the virus under certain conditions, these models do not directly translate into the interventions that can be taken by government. Neither can these models contribute to understand the economic and/or social consequences of the interventions. However, effective and sustainable solutions need to take into account this combination of factors. In this paper, we propose an agent-based social simulation tool, ASSOCC, that supports decision makers understand possible consequences of policy interventions, but exploring the combined social, health and economic consequences of these interventions.Correction available: https://link.springer.com/article/10.1007%2Fs11023-021-09565-8 (WOS:000671651200001)</p

Modely a modelování v biomedicíně

Řada vědních oborů, včetně biomedicínských disciplín, vytváří a široce využívá vědecké modely. Hlavní náplní této disertační práce je analýza některých klíčových aspektů praxe vědeckého modelování. Za prvé představím nové, komplementární pojetí vědeckého modelování (modelování založené na experimentování): teprve na této bázi lze adekvátně zachytit praxi vytváření mechanistických modelů v řadě oblastí biologického a biomedicínského výzkumu, včetně nádorové imunologie. Za druhé kriticky prozkoumám jednu z hlavních námitek vůči mechanistickému pojetí vědeckého vysvětlení: jejím cílem je ukázat, že toto pojetí nedokáže uspokojivě zachytit idealizaci rozdílových faktorů, běžně přítomnou ve vědeckém vysvětlení. Vyústěním mé argumentace bude konstatování, že tato námitka zcela selhává vinou řady konceptuálních nesrovnalostí. Za třetí analyzuji různé role, které hrají úsudky o podobnostech v té sféře výzkumu rakoviny, v níž se mechanismy podílející se na rozvoji nádorového onemocnění zkoumají za pomoci různých myších modelů. Pokusím se ukázat, že docenění komplexity těchto úsudků může být významným a zcela původním vkladem do současných filosofických debat o povaze vědecké reprezentace. Za čtvrté poukážu na fakt, že mechanismy lze efektivně zkoumat také prostřednictvím počítačových simulací. Konkrétně se...Many scientific disciplines rely on the construction and use of models: biomedical sciences are no exception. This PhD thesis addresses several aspects of the practice of scientific modeling. First, I discuss the nature of modeling as such, proposing a novel, complementary account of scientific modeling which I term the experimentation-driven modeling account and which drives the construction of mechanistic models in many fields of biological and biomedical research, such as cancer immunology. Second, I scrutinize an objection to the mechanistic account of explanation according to which the account fails to accommodate the common practice of idealizing difference-making factors. I argue that this objection ultimately fails because it is riddled with a number of conceptual inconsistencies. Third, I analyze the roles of similarity judgments in some fields of cancer research which employ a variety of mouse models to learn about the disease mechanisms, arguing that by appreciating the epistemic complexities it is possible to shed new light on more general philosophical debates regarding scientific representation. Fourth, mechanisms can also be studied using more theoretical apparatus in the form of simulations. I investigate an example of an agent-based model used to model the outbreak of SARS-CoV-2...Institute of Philosophy and Religious StudiesÚstav filosofie a religionistikyFaculty of ArtsFilozofická fakult