Munther A Dahleh
Biographic Data
| ID | 4973652 |
|---|---|
| NAME | Munther A Dahleh |
| GIVEN NAMES | Munther A |
| FAMILY NAME | Dahleh |
| SIGNATURE | DAHLEH M A |
| AFFILIATIONS | Jay Chandra is with Harvard Medical School, Harvard University, Boston, MA. Marie-Laure Charpignon and Munther A. Dahleh are with the Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge. Anushka Bhaskar and Andrew Therriault are with the Department of Government, Harvard University, Cambridge. Yea-Hung Chen is with the Department of Epidemiology and Biostatistics, University of California, San Francisco. Alyssa Mooney is with the Institute for Health Policy Studies,... |
| ORCID | 0000-0002-1470-2148 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2011 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Excess Fatal Overdoses in the United States During the Covid-19 Pandemic by Geography and Substance Type
Objectives. To assess heterogeneity in pandemic-period excess fatal overdoses in the United States, by location (state, county) and substance type. Methods. We used seasonal autoregressive integrated moving average (SARIMA) models to estimate counterfactual death counts in the scenario that no pandemic had occurred. Such estimates were subtracted from actual death counts to assess the magnitude of pandemic-period excess mortality between March 20…
Bayesian Learning in Social Networks
We study the (perfect Bayesian) equilibrium of a sequential learning model over a general social network. Each individual receives a signal about the underlying state of the world, observes the past actions of a stochastically generated neighbourhood of individuals, and chooses one of two possible actions. The stochastic process generating the neighbourhoods defines the network topology. We characterize pure strategy equilibria for arbitrary stoc…
No prominent works on this page.
Bayesian Learning in Social Networks
We study the (perfect Bayesian) equilibrium of a sequential learning model over a general social network. Each individual receives a signal about the underlying state of the world, observes the past actions of a stochastically generated neighbourhood of individuals, and chooses one of two possible actions. The stochastic process generating the neighbourhoods defines the network topology. We characterize pure strategy equilibria for arbitrary stoc…
Excess Fatal Overdoses in the United States During the Covid-19 Pandemic by Geography and Substance Type
Objectives. To assess heterogeneity in pandemic-period excess fatal overdoses in the United States, by location (state, county) and substance type. Methods. We used seasonal autoregressive integrated moving average (SARIMA) models to estimate counterfactual death counts in the scenario that no pandemic had occurred. Such estimates were subtracted from actual death counts to assess the magnitude of pandemic-period excess mortality between March 20…
Artificial Intelligence (1 work) · Artificial Intelligence (1 work) · Bayesian probability (1 work) · Complex Network Analysis Techniques (1 work) · Computer Science (1 work) · Confidence interval (1 work) · Coronavirus disease 2019 (COVID-19) (1 work) · COVID-19 and healthcare impacts (1 work) · COVID-19 and Mental Health (1 work) · Demography (1 work)