No Arabic abstract
The COVID-19 pandemic has forced changes in production and especially in human interaction, with social distancing a standard prescription for slowing transmission of the disease. This paper examines the economic effects of social distancing at the aggregate level, weighing both the benefits and costs to prolonged distancing. Specifically we fashion a model of economic recovery when the productive capacity of factors of production is restricted by social distancing, building a system of equations where output growth and social distance changes are interdependent. The model attempts to show the complex interactions between output levels and social distancing, developing cycle paths for both variables. Ultimately, however, defying gravity via prolonged social distancing shows that a lower growth path is inevitable as a result.
The 1918 influenza pandemic was characterized by multiple epidemic waves. We investigated into reactive social distancing, a form of behavioral responses, and its effect on the multiple influenza waves in the United Kingdom. Two forms of reactive social distancing have been used in previous studies: Power function, which is a function of the proportion of recent influenza mortality in a population, and Hill function, which is a function of the actual number of recent influenza mortality. Using a simple epidemic model with a Power function and one common set of parameters, we provided a good model fit for the observed multiple epidemic waves in London boroughs, Birmingham and Liverpool. Our approach is different from previous studies where separate models are fitted to each city. We then applied these model parameters obtained from fitting three cities to all 334 administrative units in England and Wales and including the population sizes of individual administrative units. We computed the Pearsons correlation between the observed and simulated data for each administrative unit. We achieved a median correlation of 0.636, indicating our model predictions perform reasonably well. Our modelling approach which requires reduced number of parameters resulted in computational efficiency gain without over-fitting the model. Our works have both scientific and public health significance.
The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has directly impacted the public health and economy worldwide. To overcome this problem, countries have adopted different policies and non-pharmaceutical interventions for controlling the spread of the virus. This paper proposes the COVID-ABS, a new SEIR (Susceptible-Exposed-Infected-Recovered) agent-based model that aims to simulate the pandemic dynamics using a society of agents emulating people, business and government. Seven different scenarios of social distancing interventions were analyzed, with varying epidemiological and economic effects: (1) do nothing, (2) lockdown, (3) conditional lockdown, (4) vertical isolation, (5) partial isolation, (6) use of face masks, and (7) use of face masks together with 50% of adhesion to social isolation. In the impossibility of implementing scenarios with lockdown, which present the lowest number of deaths and highest impact on the economy, scenarios combining the use of face masks and partial isolation can be the more realistic for implementation in terms of social cooperation. The COVID-ABS model was implemented in Python programming language, with source code publicly available. The model can be easily extended to other societies by changing the input parameters, as well as allowing the creation of a multitude of other scenarios. Therefore, it is a useful tool to assist politicians and health authorities to plan their actions against the COVID-19 epidemic.
We introduce a general Hamiltonian framework that appears to be a natural setting for the derivation of various production functions in economic growth theory, starting with the celebrated Cobb-Douglas function. Employing our method, we investigate some existing models and propose a new one as special cases of the general $n$-dimensional Lotka-Volterra system of eco-dynamics.
We study the implications of endogenous pricing for learning and welfare in the classic herding model . When prices are determined exogenously, it is known that learning occurs if and only if signals are unbounded. By contrast, we show that learning can occur when signals are bounded as long as non-conformism among consumers is scarce. More formally, learning happens if and only if signals exhibit the vanishing likelihood property introduced bellow. We discuss the implications of our results for potential market failure in the context of Schumpeterian growth with uncertainty over the value of innovations.
Social distancing as one of the main non-pharmaceutical interventions can help slow down the spread of diseases, like in the COVID-19 pandemic. Effective social distancing, unless enforced as drastic lockdowns and mandatory cordon sanitaire, requires consistent strict collective adherence. However, it remains unknown what the determinants for the resultant compliance of social distancing and their impact on disease mitigation are. Here, we incorporate into the epidemiological process with an evolutionary game theory model that governs the evolution of social distancing behavior. In our model, we assume an individual acts in their best interest and their decisions are driven by adaptive social learning of the real-time risk of infection in comparison with the cost of social distancing. We find interesting oscillatory dynamics of social distancing accompanied with waves of infection. Moreover, the oscillatory dynamics are dampened with a nontrivial dependence on model parameters governing decision-makings and gradually cease when the cumulative infections exceed the herd immunity. Compared to the scenario without social distancing, we quantify the degree to which social distancing mitigates the epidemic and its dependence on individuals responsiveness and rationality in their behavior changes. Our work offers new insights into leveraging human behavior in support of pandemic response.