scholarly journals A Poisson Autoregressive Model to Understand COVID-19 Contagion Dynamics

2020 ◽  
Author(s):  
Arianna Agosto ◽  
Paolo Giudici
Keyword(s):  
Autoregressive Model ◽  
Poisson Autoregressive Model

scholarly journals Burden of salmonellosis, campylobacteriosis and listeriosis: a time series analysis, Belgium, 2012 to 2020

2017 ◽  
Vol 22 (38) ◽  
Author(s):  
Charline Maertens de Noordhout ◽  
Brecht Devleesschauwer ◽  
Juanita A Haagsma ◽  
Arie H Havelaar ◽  
Sophie Bertrand ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Autoregressive Model ◽  
Correction Factors ◽  
Disability Adjusted Life Years ◽  
Uncertainty Interval ◽  
Food Borne ◽  
Disability Adjusted Life ◽  
Life Years ◽  
Poisson Autoregressive Model

Salmonellosis, campylobacteriosis and listeriosis are food-borne diseases. We estimated and forecasted the number of cases of these three diseases in Belgium from 2012 to 2020, and calculated the corresponding number of disability-adjusted life years (DALYs). The salmonellosis time series was fitted with a Bai and Perron two-breakpoint model, while a dynamic linear model was used for campylobacteriosis and a Poisson autoregressive model for listeriosis. The average monthly number of cases of salmonellosis was 264 (standard deviation (SD): 86) in 2012 and predicted to be 212 (SD: 87) in 2020; campylobacteriosis case numbers were 633 (SD: 81) and 1,081 (SD: 311); listeriosis case numbers were 5 (SD: 2) in 2012 and 6 (SD: 3) in 2014. After applying correction factors, the estimated DALYs for salmonellosis were 102 (95% uncertainty interval (UI): 8–376) in 2012 and predicted to be 82 (95% UI: 6–310) in 2020; campylobacteriosis DALYs were 1,019 (95% UI: 137–3,181) and 1,736 (95% UI: 178–5,874); listeriosis DALYs were 208 (95% UI: 192–226) in 2012 and 252 (95% UI: 200–307) in 2014. New actions are needed to reduce the risk of food-borne infection with Campylobacter spp. because campylobacteriosis incidence may almost double through 2020.

scholarly journals A Poisson Autoregressive Model to Understand COVID-19 Contagion Dynamics

Risks ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 77 ◽  
Author(s):  
Arianna Agosto ◽  
Paolo Giudici
Keyword(s):  
Health Economics ◽  
Statistical Model ◽  
Autoregressive Model ◽  
Trend Model ◽  
Poisson Autoregressive Model

We present a statistical model which can be employed to understand the contagion dynamics of the COVID-19, which can heavily impact health, economics and finance. The model is a Poisson autoregression of the daily new observed cases, and can reveal whether contagion has a trend, and where is each country on that trend. Model results are exemplified from some observed series.

Estimation and testing for a Poisson autoregressive model

Metrika ◽  
2009 ◽  
Vol 73 (2) ◽  
pp. 211-230 ◽  
Author(s):  
Fukang Zhu ◽  
Dehui Wang
Keyword(s):  
Autoregressive Model ◽  
Poisson Autoregressive Model

A Linear Poisson Autoregressive Model: The Poisson AR(p) Model

2001 ◽  
Vol 9 (2) ◽  
pp. 164-184 ◽  
Author(s):  
Patrick T. Brandt ◽  
John T. Williams
Keyword(s):  
Time Series ◽  
Political Science ◽  
Autoregressive Model ◽  
Alternative Model ◽  
Moving Average ◽  
Dynamic Processes ◽  
Average Model ◽  
Moving Average Model ◽  
Poisson Autoregressive Model ◽  
Weighted Moving Average

Time series of event counts are common in political science and other social science applications. Presently, there are few satisfactory methods for identifying the dynamics in such data and accounting for the dynamic processes in event counts regression. We address this issue by building on earlier work for persistent event counts in the Poisson exponentially weighted moving-average model (PEWMA) of Brandt et al. (American Journal of Political Science44(4):823–843, 2000). We develop an alternative model for stationary mean reverting data, the Poisson autoregressive model of orderp, or PAR(p) model. Issues of identification and model selection are also considered. We then evaluate the properties of this model and present both Monte Carlo evidence and applications to illustrate.

scholarly journals Zero-Inflated Time Series Modelling of COVID-19 Deaths in Ghana

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Kassim Tawiah ◽  
Wahab Abdul Iddrisu ◽  
Killian Asampana Asosega
Keyword(s):  
Time Series ◽  
Autoregressive Model ◽  
Negative Binomial ◽  
Time Series Data ◽  
Partial Likelihood ◽  
Series Data ◽  
Number Of Zeros ◽  
Time Series Modelling ◽  
Poisson Autoregressive Model ◽  
Excessive Number

Discrete count time series data with an excessive number of zeros have warranted the development of zero-inflated time series models to incorporate the inflation of zeros and the overdispersion that comes with it. In this paper, we investigated the characteristics of the trend of daily count of COVID-19 deaths in Ghana using zero-inflated models. We envisaged that the trend of COVID-19 deaths per day in Ghana portrays a general increase from the onset of the pandemic in the country to about day 160 after which there is a general decrease onward. We fitted a zero-inflated Poisson autoregressive model and zero-inflated negative binomial autoregressive model to the data in the partial-likelihood framework. The zero-inflated negative binomial autoregressive model outperformed the zero-inflated Poisson autoregressive model. On the other hand, the dynamic zero-inflated Poisson autoregressive model performed better than the dynamic negative binomial autoregressive model. The predicted new death based on the zero-inflated negative binomial autoregressive model indicated that Ghana’s COVID-19 death per day will rise sharply few days after 30th November 2020 and drastically fall just as in the observed data.

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