Evaluating the effect of early policy responses on the COVID-19 pandemic: prediction model from data in Vietnam
Infectious Diseases and Tropical Medicine 2023;
9: e1076
DOI: 10.32113/idtm_20232_1076
Topic: COVID-19
Category: Original article
Abstract
OBJECTIVE: Early responses to COVID-19 have been conducted by Vietnam’s government in both the speed and the stringency of the interventions used. To evaluate the effect of these policies, we built the predictive model to compare the actual cases and estimated cases in Vietnam.
SUBJECTS AND METHODS: We applied the predictive model with two parameters at three points before the travel restriction policy, after one week, and after the policy’s end (2 weeks). After each time of evaluation, two parameters in the model were adjusted to estimate new cases in the following time. Nonlinear Least Squares determined the nonlinear (weighted) least-squares estimates of the parameters of a nonlinear model.
RESULTS: Many measures were applied to prevent the spread of the pandemic, and two highlighted measures were quarantine and social distancing policy. Parameters were estimated before the policy (a = 20.686, b = 0.098), after the promulgation of policy (a = 23.179, b = 0.089), and after one week (a = 30.759, b = 0.072). The difference between expected and observed cases was statistically significant (p = 0.01), showing positive results of the policies. After one week (the incubation period), suspected and infected cases have been detected and managed, facilitating the reduction of new cases.
CONCLUSIONS: The effects of early policy response on the COVID-19 pandemic were significant after each stage of serial measurements, according to the parameters of the predictive model. Our model can be considered in the next wave of the COVID-19 pandemic or another pandemic to predict progress and take measures effectively.
SUBJECTS AND METHODS: We applied the predictive model with two parameters at three points before the travel restriction policy, after one week, and after the policy’s end (2 weeks). After each time of evaluation, two parameters in the model were adjusted to estimate new cases in the following time. Nonlinear Least Squares determined the nonlinear (weighted) least-squares estimates of the parameters of a nonlinear model.
RESULTS: Many measures were applied to prevent the spread of the pandemic, and two highlighted measures were quarantine and social distancing policy. Parameters were estimated before the policy (a = 20.686, b = 0.098), after the promulgation of policy (a = 23.179, b = 0.089), and after one week (a = 30.759, b = 0.072). The difference between expected and observed cases was statistically significant (p = 0.01), showing positive results of the policies. After one week (the incubation period), suspected and infected cases have been detected and managed, facilitating the reduction of new cases.
CONCLUSIONS: The effects of early policy response on the COVID-19 pandemic were significant after each stage of serial measurements, according to the parameters of the predictive model. Our model can be considered in the next wave of the COVID-19 pandemic or another pandemic to predict progress and take measures effectively.
To cite this article
Evaluating the effect of early policy responses on the COVID-19 pandemic: prediction model from data in Vietnam
Infectious Diseases and Tropical Medicine 2023;
9: e1076
DOI: 10.32113/idtm_20232_1076
Publication History
Submission date: 02 Oct 2022
Revised on: 30 Oct 2022
Accepted on: 23 Dec 2022
Published online: 28 Feb 2023
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.