This dissertation analyzes the interactions between infectious diseases and the economy. The different chapters theoretically and empirically examine different channels of interaction. Within each chapter we initially provide a theoretical foundation which builds upon extensions and adaptations of existing models and partly new models. In a second step we present the empirical analyses and the corresponding results using modern empirical methods. We analyze HIV/AIDS, the Spanish Flu and infectious diseases at the workplace. HIV/AIDS and the Spanish Flu are special since they mainly affect prime-age individuals, and thus have considerable effects on labor supply. In the first half of this dissertation we investigate how infectious diseases affect the economy. In the second half the reverse relationship is analyzed, namely how the business cycle affects the spread of diseases. In the last chapter, we briefly summarize the results and investigate paths for further research. The second chapter analyzes how HIV prevalence affects the Gross Domestic Product (GDP) in the twelve most heavily affected countries, i.e. the countries which have ever exhibited a HIV prevalence rate of 10% or more. Here the analysis is divided into a theoretical approach following a Solow growth model and an empirical approach using data and results from previous empirical studies on how different determinants affect the growth path of a country. The twelve countries are analyzed by means of synthetic control groups, a new method developed by Alberto Abadie and co-authors. For every country under consideration a synthetic control group is formed, consisting of a weighted average of donor countries not affected by HIV (HIV prevalence rate smaller than 1%). The results in the different countries are very heterogeneous. While some countries show no effect at all, other countries exhibit a reduction in GDP between 25 and 77%, compared to the scenario without HIV. Since we reveal very different effects in chapter 2, we try to restrict the possible channels in chapter 3 by analyzing demographic effects of HIV. More specifically, in this chapter we estimate how HIV affects life expectancy, and death and birth rates. In order to find better control groups the synthetic control group method is extended to several variables. As expected there are very clear-cut effects on life expectancy and the death rate. Average life expectancy decreased by almost 15 years, while the death rate increased by seven deaths on average (per 1,000 inhabitants) due to HIV. In terms of the birth rate the effects are once more quite heterogeneous. On average we estimate a very small and statistically insignificant effect. Due to the effect heterogeneity between countries we observe in the first chapters, we provide an in depth analysis of one country in chapter 4. One reason for the effect heterogeneity is that different countries took different measures to prevent the spread of the disease. Therefore, in this chapter we analyze the economic consequences of the Spanish Flu -- a disease that occurred very rapidly and thus prevention was very limited. The disease spread between 1918 and 1920. Due to the time period it is difficult to separate effects of the Spanish Flu from the consequences of World War I. Therefore the analysis focuses on a country which was neutral during the War: Sweden. Other reasons for analyzing the effects on Sweden are the good data availability and vast differences in terms of prevalence and mortality between Swedish counties. These differences are used to measure the effects of the Spanish Flu. The results show that capital income decreased more in counties with more flu-related deaths, while poverty increased. However, there is robust evidence that the influenza had no discernible effect on earnings. This finding is surprising since it goes against most previous empirical studies as well as theoretical predictions. In chapter 5 the causal channel is reversed and we analyze how the business cycle affects the spread of diseases. The procyclical nature of sickness absence has been documented by many scholars in the literature. So far, the explanations have been based on labor force composition and moral hazard. We propose and test a third mechanism caused by presenteeism (i.e. working while sick) and infections. We suggest that the workload is higher during an economic boom and thus employees go to work even when they do not feel well. In a theoretical model focusing on infectious diseases, we show that this provokes infections of co-workers leading to overall higher sickness absence during economic upturns. Using county-level aggregated data from 112 German public sickness funds (out of 145 in total) we confirm this hypothesis by showing that infectious diseases show the largest procyclical pattern.