Abstract
Disease contraction and recovery depend on complex interaction between persons potentially contracting and recovering from the disease, the pharmaceutical industry potentially developing drugs, and donors potentially subsidizing drug development and drug purchases. Instead of analyzing each of these three kinds of players separately, assuming the behavior of the other two kinds of players to be given, this article analyzes the three kinds of players holistically and how they mutually interact and react to each other. A five-period game between N persons and a pharmaceutical company is developed. Each person chooses safe or risky behavior, and whether or not to buy a drug. The objectives are to determine which strategies the N persons and the pharmaceutical company choose depending on the model parameters. The pharmaceutical company develops the drug if sufficiently many persons contract the disease and buy the drug. A donor chooses parametrically whether to subsidize drug development and drug purchases. Nature chooses probabilistically disease contraction, and recovery versus death with and without applying the drug. The methodological procedure is to solve the game with backward induction specifying the conditions for each of five outcomes ranging from safe behavior to risky behavior and buying the drug. The results in the form of five outcomes for a person are safe behavior, risky behavior and no disease contraction, disease contraction without drug availability, disease contraction with drug availability but without buying the drug, and disease contraction and buying the drug. These five outcomes are spread across two outcomes for the pharmaceutical company which are not to develop versus to develop the drug. The utility for the donor is specified for these two outcomes. A procedure for estimating the parameters is presented based on HIV/AIDS data. The results are discussed in terms of how various parameter combinations cause the five outcomes. An example illustrates the players’ strategic choices.
Highlights
IntroductionMoxnes and Hausken (2012) model with differential time equations the immune system and the virus dynamics of acute virus influenza A infections, showing good agreement with the evolution of the 1918 Spanish flu virus H1N1
Hospitals, and other players in the national and international health system and political system are not explicitly present in the model as players since we focus on the strategic interaction between N persons and the pharmaceutical company, parametrically impacted by a donor and Nature
Moxnes and Hausken (2012) model with differential time equations the immune system and the virus dynamics of acute virus influenza A infections, showing good agreement with the evolution of the 1918 Spanish flu virus H1N1. Their non-game theoretic analysis is relevant for drug development of both vaccines for prevention and drugs for treatment, which in turn impacts the game between the pharmaceutical company and persons analyzed in this article
Summary
Moxnes and Hausken (2012) model with differential time equations the immune system and the virus dynamics of acute virus influenza A infections, showing good agreement with the evolution of the 1918 Spanish flu virus H1N1 Their non-game theoretic analysis is relevant for drug development of both vaccines for prevention and drugs for treatment, which in turn impacts the game between the pharmaceutical company and persons analyzed in this article. The international community, and Nature choosing disease contraction and recovery International influenza vaccination coordination Incentives for developing drugs for treatment seem stronger than incentives for developing vaccines for prevention Global costs, health achievements, and economic benefits of twenty years of ART Estimation of $2.6 billion for HIV drug R&D costs during 2017–2021 Revenues for HIV/AIDS treatment during 2017–2021 for some African countries Financing of the response to HIV/AIDS in low-income and middle-income countries Cost-effectiveness of HIV treatment in resource-poor settings Summarize modeling approaches. It chooses the disease contraction probability q in period 2, the recovery probability x in period 4 if no drug is developed, the same recovery probability x in period 5 if the drug is developed but not bought (and not applied), and the recovery probability
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
