Varicella Incidence Rate Forecasting in Bogotá D.C. (Colombia) by Stochastic Time Series Analysis

Abstract India implemented a national mandatory lockdown policy (Lockdown 1.0) on 24 March 2020 in response to Coronavirus Disease 2019 (COVID-19). The policy was revised in three subsequent stages (Lockdown 2.0–4.0 between 15 April to 18 May 2020), and restrictions were lifted (Unlockdown 1.0) on 1 June 2020. This study evaluated the effect of lockdown policy on the COVID-19 incidence rate at the national level to inform policy response for this and future pandemics. We conducted an interrupted time series analysis with a segmented regression model using publicly available data on daily reported new COVID-19 cases between 2 March 2020 and 1 September 2020. National-level data from Google Community Mobility Reports during this timeframe were also used in model development and robustness checks. Results showed an 8% [95% confidence interval (CI) = 6–9%] reduction in the change in incidence rate per day after Lockdown 1.0 compared to prior to the Lockdown order, with an additional reduction of 3% (95% CI = 2–3%) after Lockdown 4.0, suggesting an 11% (95% CI = 9–12%) reduction in the change in COVID-19 incidence after Lockdown 4.0 compared to the period before Lockdown 1.0. Uptake of the lockdown policy is indicated by decreased mobility and attenuation of the increasing incidence of COVID-19. The increasing rate of incident case reports in India was attenuated after the lockdown policy was implemented compared to before, and this reduction was maintained after the restrictions were eased, suggesting that the policy helped to ‘flatten the curve’ and buy additional time for pandemic preparedness, response and recovery.

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Association between weather factors and dengue in Dhaka: a time-series analysis

Journal of Preventive and Social Medicine ◽

10.3329/jopsom.v39i1.51861 ◽

2021 ◽

Vol 39 (1) ◽

pp. 43-49

Author(s):

Shafia Shaheen

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Incidence Rate ◽

Negative Binomial ◽

Weather Data ◽

Negative Binomial Regression Model ◽

Weather Factors ◽

Average Temperature ◽

Series Analysis ◽

Average Maximum

Background: There was an epidemic of dengue fever that happened in Bangladesh in the year of 2019. Temperature of this country has been raising which leads to changing in rainfall pattern. This study was aimed to investigate the relationship of weather factors and dengue incidence in Dhaka. Methods: A time series analysis was carried out by using 10 years weather data as average , maximum and minimum monthly temperature, average monthly humidity and average and cumulative monthly rainfall. Reported number of dengue cases was extracted from January 2009 to July 2019. Firstly, dengue incidence rate was calculated. Correlation analysis and negative binomial regression model was developed. Results: Dengue incidence rate had sharp upward trend. Dengue incidence and mean, maximum and minimum average temperature showed statistically significant negative correlation at 3 months' lag. Highest incidence Rate Ratio (IRR) of dengue was found at minimum average temperature at 0 and I-month lag. Average humidity showed positive and significant correlation with dengue incidence at 0-month lag. Average and cumulative rainfall also showed negative and significant correlation only at 3-months lag period. Conclusion: Weather variability influences dengue incidence and the association between the weather factors are non linear and not consistent. So the study findings should be evaluated area basis with other local factors to develop early warning for dengue epidemic prediction. JOPSOM 2020; 39(1): 43-49

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Incidence of hip and knee replacement in patients with rheumatoid arthritis following the introduction of biological DMARDs: an interrupted time-series analysis using nationwide Danish healthcare registers

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2017-212424 ◽

2017 ◽

Vol 77 (5) ◽

pp. 684-689 ◽

Cited By ~ 27

Author(s):

René Lindholm Cordtz ◽

Samuel Hawley ◽

Daniel Prieto-Alhambra ◽

Pil Højgaard ◽

Kristian Zobbe ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Time Series ◽

Time Series Analysis ◽

Incidence Rate ◽

Interrupted Time Series ◽

Incidence Rates ◽

Interrupted Time Series Analysis ◽

Series Analysis ◽

National Patient ◽

The Impact

ObjectivesTo study the impact of the introduction of biological disease-modifying anti-rheumatic drugs (bDMARDs) and associated rheumatoid arthritis (RA) management guidelines on the incidence of total hip (THR) and knee replacements (TKR) in Denmark.MethodsNationwide register-based cohort and interrupted time-series analysis. Patients with incident RA between 1996 and 2011 were identified in the Danish National Patient Register. Patients with RA were matched on age, sex and municipality with up to 10 general population comparators (GPCs). Standardised 5-year incidence rates of THR and TKR per 1000 person-years were calculated for patients with RA and GPCs in 6-month periods. Levels and trends in the pre-bDMARD (1996–2001) were compared with the bDMARD era (2003–2016) using segmented linear regression interrupted by a 1-year lag period (2002).ResultsWe identified 30 404 patients with incident RA and 297 916 GPCs. In 1996, the incidence rate of THR and TKR was 8.72 and 5.87, respectively, among patients with RA, and 2.89 and 0.42 in GPCs. From 1996 to 2016, the incidence rate of THR decreased among patients with RA, but increased among GPCs. Among patients with RA, the incidence rate of TKR increased from 1996 to 2001, but started to decrease from 2003 and throughout the bDMARD era. The incidence of TKR increased among GPCs from 1996 to 2016.ConclusionWe report that the incidence rate of THR and TKR was 3-fold and 14-fold higher, respectively among patients with RA compared with GPCs in 1996. In patients with RA, introduction of bDMARDs was associated with a decreasing incidence rate of TKR, whereas the incidence of THR had started to decrease before bDMARD introduction.

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