Additional file 2 of Associations between environmental covariates and temporal changes in malaria incidence in high transmission settings of Uganda: a distributed lag nonlinear analysis
Creators: name: Okiring, Jaffer
name: Routledge, Isobel
name: Epstein, Adrienne
name: Namuganga, Jane F.
name: Kamya, Emmanuel V.
name: Obeng-Amoako, Gloria Odei
name: Sebuguzi, Catherine Maiteki
name: Rutazaana, Damian
name: Kalyango, Joan N.
name: Kamya, Moses R.
name: Dorsey, Grant
name: Wesonga, Ronald
name: Kiwuwa, Steven M.
name: Nankabirwa, Joaniter I.
Publisher: name: figshare
Year: 2021
Subjects: subject: 29999 Physical Sciences not elsewhere classified
subject: FOS: Physical sciences
subject: Medicine
subject: Biotechnology
subject: Ecology
subject: FOS: Biological sciences
subject: Cancer
subject: Science Policy
subject: 110309 Infectious Diseases
subject: FOS: Health sciences
subject: Computational Biology

description: Additional file 2: Fig. S2. a. Estimated incidence risk ratios (IRR) for malaria as a function of temperature obtained from distributed lag models, over 1000 simulations. Estimates from the same simulation run are connected with gray lines. The red thick line represents the IRRs observed in the real dataset. Results are presented for temperatures 26 °C, 35 °C, 45 °C, taking 30 °C as a reference. The results were obtained when simulating data with the following IRRs: At temperature 26 °C: IRR = 1 at lags 0 and 2, IRR = 1.31 at lag 1, IRR = 1.27 at lag 3, and IRR = 1.24 at lag 4; at temperature 35 °C: IRR = 1.28 for lags 0, IRR = 1.45 for lags 1, IRR = 1.24 for lags 2, IRR = 1.70 at lag 3, IRR = 1.24 at lag 4; at temperature 45 °C: IRR =1.29 at lag 0, IRR = 1 for lags 1 and lag 4, IRR = 1.07 at lag 2, IRR =1.48 at lag 3. b. Estimated IRR for malaria as a function of rainfall obtained from distributed lag models, over 1000 simulations. Estimates from the same simulation run are connected with gray lines. The red thick line represents the RRs observed in the real dataset. Results are presented for rainfall 3 mm, 200 mm, and 247 mm, taking 133 mm as a reference. The results were obtained when simulating data with the following IRRs: At rainfall 3 mm: IRR = 1 at lags 0,2 and 3, IRR = 1.15 at lag 1, and IRR = 1.60 at lag 4; at rainfall 200 mm: IRR = 1.12 for lags 0, IRR = 1 at lags 1,3 and 4, IRR = 1.03 at lag 2; at rainfall 247 mm: IRR =1 at lag 0, IRR = 2.13 at lag 1, IRR = 1.46 at lag 2, IRR = 1.95 at lag 3, IRR = 2.20 at lag 4. c. Estimated IRR for malaria as a function of NDVI obtained from distributed lag models, over 1000 simulations. Estimates from the same simulation run are connected with gray lines. The red thick line represents the RRs observed in the real dataset. Results are presented for NDVI values of 0.24, 0,50,0.72, taking 0.66 as a reference. The results were obtained when simulating data with the following IRRs: At NDVI 0.24: IRR = 1 at lags 0 and 1, IRR = 1.04 at lag 2,IRR = 1.98 at alg3, and IRR = 1.37 at lag 4; at NDVI 0.50: IRR = 1.13 for lags 0, IRR = 1.01 at lag 1, IRR = 1 at lag 2,IRR = 1.22 at lag 3, and IRR = 1.34 at lag 4; at NDVI 0.72: IRR =1.17 at lag 0, IRR = 1 at lag 1 and 3, IRR 1.12 at lag 2, IRR = 1.07 at lag 4.