Seasonality of influenza and coseasonality with avian influenza in Bangladesh, 2010–19: a retrospective, time-series analysis

Berry, Isha; Rahman, Mahbubur; Flora, Meerjady Sabrina; Shirin, Tahmina; Alamgir, A. S. M.; Khan, Manjur Hossain; Anwar, Rubaid; Lisa, Mona; Chowdhury, Fahmida; Islam, Md. Ariful; Osmani, Muzzafar G; Dunkle, Stacie; Brum, Eric; Greer, Amy L.; Morris, Shaun K.; Mangtani, Punam; Fisman, David N.

doi:10.1016/s2214-109x(22)00212-1

Cited by 20 publications

(15 citation statements)

References 30 publications

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“…4 B and C) and peaks the latest in the west. We also noted that influenza tends to peak earliest in the capital city Dhaka in the sentinel surveillance data, similar to previous findings ( 21 , 22 ). The observed spatial gradient is accurately captured in our simulated incidence, although with a slight lag in the center of gravity of outbreaks (Fig.…”

Section: Resultssupporting

confidence: 91%

“…The test-positivity measure may also be biased, and may not accurately reflect the spatiotemporal pattern of influenza incidence. Nonetheless, the observed pattern in our test-positivity measure is consistent with the strong seasonal and spatial dynamics that have been documented previously for Bangladesh ( 21 , 22 ). Additionally, in settings where both test-positivity and other measures of influenza burden are available, the temporal dynamics of the test-positivity measure closely matches the temporal dynamics of other measures such as the number of hospitalized cases ( 27 , 28 ).…”

Section: Discussionsupporting

confidence: 91%

“…Although the environmental drivers of influenza have been well studied in high-income settings in temperate regions, many open questions remain about the burden, seasonality, and drivers of influenza dynamics in the tropics. Consistent with previous studies from Bangladesh ( 21 , 22 , 26 ), we document annual seasonal peaks during the monsoons and a spatial gradient in the timing of peaks. The novel contributions of our study are to identify key drivers of these observed patterns that have remained a puzzle in the literature to date, and to disentangle the effect of future climate conditions on the transmission of influenza in Bangladesh.…”

Section: Discussionsupporting

confidence: 90%

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The impact of current and future climates on spatiotemporal dynamics of influenza in a tropical setting

Mahmud,

Martinez,

Baker

2023

PNAS Nexus

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Although the drivers of influenza have been well-studied in high-income settings in temperate regions, many open questions remain about the burden, seasonality, and drivers of influenza dynamics in the tropics. In temperate climates, the inverse relationship between specific humidity and transmission can explain much of the observed temporal and spatial patterns of influenza outbreaks. Yet, this relationship fails to explain seasonality, or lack there-of, in tropical and sub-tropical countries. Here, we analyzed eight years of influenza surveillance data from 12 locations in Bangladesh to quantify the role of climate in driving disease dynamics in a tropical setting with a distinct rainy season. We find strong evidence for a non-linear bimodal relationship between specific humidity and influenza transmission in Bangladesh, with highest transmission occurring for relatively low and high specific humidity values. We simulated influenza burden under current and future climate in Bangladesh using a mathematical model with a bimodal relationship between humidity and transmission, and decreased transmission at very high temperatures, while accounting for changes in population immunity. The climate-driven mechanistic model can accurately capture both the temporal and spatial variation in influenza activity observed across Bangladesh, highlighting the usefulness of mechanistic models for low-income countries with inadequate surveillance. By using climate model projections, we also highlight the potential impact of climate change on influenza dynamics in the tropics and the public health consequences.

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Section: Resultssupporting

confidence: 91%

Section: Discussionsupporting

confidence: 91%

Section: Discussionsupporting

confidence: 90%

See 1 more Smart Citation

The impact of current and future climates on spatiotemporal dynamics of influenza in a tropical setting

Mahmud,

Martinez,

Baker

2023

PNAS Nexus

View full text Add to dashboard Cite

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“…In uenza-like case surveillance is based on symptom surveillance at the group level, and pathogen surveillance is the gold standard of in uenza epidemic surveillance [16]. In uenza-like case surveillance has been proved to be e cient, sensitive and timely [17][18][19]. In uenza-like case surveillance and pathogen monitoring are performed before the legal report of in uenza positive cases in China.…”

Section: Discussionmentioning

confidence: 99%

Comparative study on influenza time series prediction models in a megacity from 2010 to 2019: Based on SARIMA and deep learning hybrid prediction model

Yang

et al. 2022

Preprint

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Background: It is very important to establish more accurate influenza prediction models in megacities. The purpose of this study was to compare the different time series prediction models for influenza from 2010 to 2019 in Beijing, China. Methods: We took the influenza-like illness rate (ILI%), the influenza positive rate and the product of ILI% and influenza positive rate as dependent variables respectively. Subsequently, and model performances of summer point, peak bottom point and peak rising point were analyzed. After selecting the best prediction point, we compared the model performances of different parameters at that point using the SARIMA model. Then, the best model selected by SARIMA was compared with the hybrid LSTM model. Results: Between the 26th week of 2010 and the 25th week of 2019, there were 6,753,116(1.24%) ILI patients, 15,883(16.75%) of which were positive for influenza.The trends and the peak times of ILI%, the influenza positive rate and the product of ILI% and influenza positive rate were roughly the same. The SARIMA model of the peak rising point was better than those of the summer point and peak bottom point. The hybrid LSTM model performed better than the selected best SARIMA model in terms of ILI%, influenza positive rate and the product of ILI% and influenza positive rate. Also, the hybrid LSTM model could maintain a good prediction effect from the 1st to the 26th week.. On the contrary, the prediction effect of the SARIMA model decreased significantly with the extension of the prediction period. Conclusions: Our results suggested that the prediction effect of the hybrid LSTM model was better than the SARIMA model, in terms of ILI%, influenza positive rate and the product of ILI% and influenza positive rate. SARIMA was more suitable for short-term prediction, while the hybrid LSTM model showed obvious advantages in long-term prediction. Our research could help to improve the prediction and early warning of influenza and other respiratory infectious diseases.

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“…However, the absence of winter in tropical regions makes the yearly patterns of influenza and influenza-like illness less seasonal and less predictable 1 . Thus far, it has been found that influenza in tropical regions shows lower variation in incidence as well as different periodicities temporally 2 and spatially [3][4][5][6] , making it difficult to forecast periods of high incidence. Although some studies in tropical areas have shown associations between climate or environmental factors and influenza transmission [7][8][9] , this relationship remains elusive 1,10 and caution is needed in interpreting these results and drawing inferences on a 'tropical influenza season'.…”

Section: Introductionmentioning

confidence: 99%

A combination of annual and nonannual forces drive respiratory disease in the tropics

Yang

Servadio

Thanh

et al. 2023

Preprint

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Background: It is well known that influenza and other respiratory viruses are wintertime-seasonal in temperate regions. However, respiratory disease seasonality in the tropics remains elusive. In this study, we aimed to characterize the seasonality of influenza-like illness (ILI) and influenza virus in Ho Chi Minh City (HCMC), Vietnam. Methods: We monitored the daily number of ILI patients in 89 outpatient clinics from January 2010 to December 2019. We collected nasal swabs and tested for influenza from a subset of clinics from May 2012 to December 2019. We used spectral analysis to describe the periodicities in the system. We evaluated the contribution of these periodicities to predicting ILI and influenza patterns through lognormal and gamma hurdle models. Findings: During ten years of community surveillance, 66,799 ILI reports were collected covering 2.9 million patient visits; 2604 nasal swabs were collected 559 of which were PCR-positive for influenza virus. Both annual and nonannual cycles were detected in the ILI time series, with the annual cycle showing 8.9% lower ILI activity (95% CI: 8.8%-9.0%) from February 24 to May 15. Nonannual cycles had substantial explanatory power for ILI trends (ΔAIC = 183) compared to all annual covariates (ΔAIC = 263). Near-annual signals were observed for PCR-confirmed influenza but were not consistent along in time or across influenza (sub)types. Interpretation: Our study reveals a unique pattern of respiratory disease dynamics in a tropical setting influenced by both annual and nonannual drivers. Timing of vaccination campaigns and hospital capacity planning may require a complex forecasting approach. Funding: National Institutes of Health, Wellcome Trust.

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Seasonality of influenza and coseasonality with avian influenza in Bangladesh, 2010–19: a retrospective, time-series analysis

Cited by 20 publications

References 30 publications

The impact of current and future climates on spatiotemporal dynamics of influenza in a tropical setting

The impact of current and future climates on spatiotemporal dynamics of influenza in a tropical setting

Comparative study on influenza time series prediction models in a megacity from 2010 to 2019: Based on SARIMA and deep learning hybrid prediction model

A combination of annual and nonannual forces drive respiratory disease in the tropics

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