Table 1.
| Ref. | Author, year | Study period (year) | City (Country) | Exposure | Statistical model | Unit of data |
Confounder control | Variation in susceptible population | Autocorrelation* | Assessed Lag* | Overdispersion | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Season | Trend | Others | ||||||||||||
| Malaria | 7 | Kim, et al., 2012 | 2001–2009 | the capital region (Korea) | temperature, RH, diurnal temperature range (DTR), duration of sunshine | GLM Poisson | weekly | Fourier terms | year | — | — | — | 0 to 8 weeks single lag (SL) for all cliamte parameters, rainfall 0 to 60 days (SL) | Overdispersion parameter included |
| 8 | Jusot, et al., 2011 | 2000–2003 | Magaria (Niger) | rainfall | GAM negative binomial (NB) | daily | penalised cubic regression spline | religious celebrations, days of the week, holidays, min & max temp, RH | — | penalised cubic regression spline is to minimize the autocorrelation | 0 to 40 days (SL) | NB distribution model | ||
| 9 | Haque, et al., 2010 | 1989–2008 | Rangamati district, (Bangladesh) |
temperature, rainfall, humidity, normalized difference vegetation index (NDVI), SST of the Bay of Bengal, NINO3 | GLM NB | monthly | month | year | — | — | AR(1) included | all (except NINO): 0 to 3 months moving average (MA), NINO3: 0 to 3, 4 to 7, 8 to 11 (MA) | NB distribution model | |
| 10 | Xiao, et al., 2010 | 1995–2006 | Hinan (China) | temperature, rainfall, RH | Poisson regression | monthly | — | — | population | — | the cases for the previous months | 0 to 3 months (SL) | — | |
| 11 | Olson, et al., 2009 | 1996–1999 | Brazilian Amazon region | temperature, rainfall | Poisson regression | monthly | natural cubic spline | population (offset) | — | — | — | — | ||
| 12 | Hashizume, et al., 2008 | 1982–2011 | western Kenyan highlands | DMI (diapole mode index), NINO3, rainfall | GLM Poisson | monthly | month | year | population not considered since trends in malaria rates are included in the model | — | AR(1) included | 0 to 6 months (SL) | included overdispersion parameter | |
| 13 | Teklehaimanot, et al., 2004 | 1990–2000 | Ethiopia | temperature, rainfall | Poisson regression | weekly | week (of the year) | — | — | — | AR included (based on a moving average of the number of cases four, five and six weeks before) | rainfall: 4 to 12 weeks (MA) temperature: 4 to 10 weeks (MA) | — | |
| 14 | Teklehaimanot, et al., 2004 | 1990–2000 | Ethiopia | temperature, rainfall | Poisson regression | weekly | time variable | — | district, interaction between time and district | — | — | rainfall: 4 to 12 weeks (MA) temperature: 3 to 10 weeks (MA) | — | |
| 15 | Abeku, et al., 2003 | 1986–1993 | Ethiopia | temperature, rainfall | GLMM (mixed model) | monthly | — | — | log (numer of cases in the previous month) was included as sector-specific random effects | — | log (numer of cases in the previous month) as sector-specific random effects handles spatial and temporal autocorrelations. | rainfall: 1 and 2 months distributed lag (DL) temperature: 1 month (SL) | — | |
| Dengue | 16 | Hii, et al., 2012 | 2000–2011 | Singapore | temperature, rainfall | Poisson regression | weekly | season parameter | trend parameter | population (offset) | — | the past number of cases | 12 to 24 weeks (SL) | developed Poisson regression model that allowed overdispersion |
| 17 | Gomes, et al., 2012 | 2001–2009 | Rio de Janeiro (Brazil) | rainfall, temperature, proportions of days in the month: mean temperature < 22(°C), 22 ≤ mean temperature < 26, 26 ≤ mean temperature | GLM Poisson & NB | monthly | — | year | population × the number of days in the month (offset) | — | — | 1 and 2 months (SL) | NB distribution model | |
| 18 | Lowe, et al., 2011 | 2001–2009 | Southeast Brazil | rainfall, temperature, Oceanic Niño Index (ONI) | GLMM NB | monthly | month | — | expected number (offest): the population × global dengue rate. cartographic, demographic, and economic variables | inclusion of unstructured random effect to be surrogate for not only population immunity, but quality of healthcare services and local health interventions | the log standardised morbidity ratio lagged by 3 months was included in the model. | temperature and rain: 3 month (MA), ONI: 4 month (SL) | NB distribution model | |
| 19 | Hashizume, et al., 2012 | 2005–2009 | Dhaka (Bangladesh) | river levels, temperature, rainfall | GLM Poisson | weekly | Fourier terms | year | public holidays | — | AR(1) included | assessed up to 26 weeks | used generalized linear Poisson regression models allowing for overdispersion | |
| 20 | Earnest, et al., 2012 | 2001–2008 | Singapore | temperature, rainfall, RH, ours of sunshine and hours of cloud, Southern Oscillation Index (SOI) | Poisson regression | weekly | sinusoidal terms | — | — | — | AR(2) included | 0 to 12 week (SL) | included overdispersion parameter | |
| 21 | Pham, et al., 2011 | 2004–2008 | Dak Lak province, Vietnam | temperature, duration of sunshine, rainfall, RH, larval index (household index, the container index, and the Breteau index) | Poisson regression | monthly | Seasonal components | Trend components | — | — | AR(1) included | — | — | |
| 22 | Pinto, et al., 2011 | 2000–2007 | Singapore | rainfall, temperature, RH | Poisson regression | weekly | — | — | — | — | — | 0 to 40 week (SL) | — | |
| 23 | Shang, et al., 2010 | 1998–2007 | 3 areas in Southern Taiwan (Tinan, Kaohsiung, and Pingtung) | temperature, RH, wind speed, rainfall, rainy hours, sunshine accumulation hours, sunshine rate (from sunrise to sunset), sunshine total flux, imported dengue cases | Poisson regression, and GLM NB | bi-weekly | Fourier terms | — | area, population desity | — | — | assessed 1 to 12 bi-weeks which is equivalent to 2 tp 24 weeks (SL) | NB distribution model | |
| 24 | Chen, et al., 2010 | 1998–2008 | Taipei and Kaohsiung (Taiwan) | temperatures, rainfall intensity, RH | Poisson regression, GEE | monthly | — | — | the percentage of monthly Breteau index (BI) levels > 2 (index for the potential transmission risk) | — | — | 0 to 4 months (SL) | — | |
| 25 | Tipayamongkholgul, et al., 2009 | 1996–2005 | all provinces in Thailand | the multivariate ENSO index (MEI), the sea level pressure index (SLP), temperatures, RH, wind speed | quasi-Poisson or NB | monthly | sinusoidal terms | population (offset), province, population density | — | the cases of the previous month | 1 to 12 months (SL) | used quasi-Poisson or NB | ||
| 26 | Lu, et al., 2009 | 2001–2006 | Guangzhou (China) | temperatures, rainfall, RH, wind velocity | Poisson regression, GEE | monthly | — | — | — | AR(1) included | 0 to 3 months (SL) | included overdispersion parameter | ||
| 27 | Johansson, et al., 2009 | 1986–2006 | all manicipalities in Puerto Rico | temperatures, rainfall | Poisson regression | monthly | natural cubic spline on observational time | population (offest), % of population below the poverty line | — | — | temperature: 0 to 2 month (DL), rain: 1 to 2 (DL) | — | ||
| 28 | Thammapalo, et al., 2005 | 1978–1997 | 73 provinces in Thailand | rainfall, rainny days, temperatures, RH | Poisson regression | monthly | Fourier terms | time in month (t) and (t)2 | — | — | the lagged residual series is included | none | — | |
| Cholera | 29 | Hashizume, et al., 2011 | 1993–2007 | Dhaka (Bangladesh) | DMI, NINO3, SST and SSH of the northern Bay of Bengal | GLM negative binomial (NB) | monthly | month | year | — | not considered | lagged model residual included (Brumback method) | 0–3, 4–7, 8–11 months (MA) | NB distribution model |
| 30 | Rajendran, et al., 2011 | 1996–2008 | Kolkata (India) | temperature, RH, rainfall | GLM, SARIMA | daily | exponential smoothing function | — | — | — | — | — | ||
| 31 | Hashizume, et al., 2010 | 1983–2008 | Dhaka (Bangladesh) | temperature, rainfall | GLM Poisson | weekly | Fourier terms | year | sampling proportion | — | — | high rain: 0–8 (MA), low rain: 0–16 (MA), temperature: 0–4 (MA) | included overdispersion parameter | |
| 32 | Paz, 2009 | 1971–2006 | 8 African countries: Uganda, Kenya, Rwanda, Burundi, Tanzania, Malawi, Zambia, and Mozambique | air temperature, sea surface temperature (the western Indian Ocean), anomaly air temperature | Poisson regression | yearly | — | — | — | — | AR1 = cor (Yt, Yt-1) is taken into account in the estimation using generalized estimating equations. | 0 and 1 year (SL) | — | |
| 33 | Constantin de Magny, et al., 2008 | 1997–2006 | Matlab (Bangladesh) and Kolkata (India) | SST, rain, chlorophyll a concetration | GLM quasi-Possion | monthly | quarter periods of a year | — | — | — | log (number of cases for the previous month) | 0 and 1 month (SL) | quasi-Poisson model | |
| 34 | Martinez-Urtaza, et al., 2008 | 1994–2005 | Peru | SST, sea height anmoaly, heat content above 20°C | GAM NB & ridge regression with penalties to identify zero-inflation | weekly | thin plate regression splines | — | — | observational time × smoothing (when autocorrelation was seen in residuals) included | 1 to 5 weeks (SL) | NB distribution model | ||
| 35 | Luque Fernández, et al., 2008 | 2003–2006 | Lusaka (Zambia) | temperature, rainfall | GLM Poisson | weekly | sinusoidal terms | — | — | — | the cases for the previous week. | temperature 6 weeks (SL), rainfall 3 weeks (SL) | examined by standard errors were scaled using the square root of the Pearson chi2 dispersion. | |
| 36 | Hashizume, et al., 2008 | 1996–2002 | Dhaka (Bangladesh) | rainfall, river level, temperature | GLM Poisson | weekly | Fourier terms | year | public holidays | — | AR(1) included | rainfall: 0 to 16 weeks (MA), river level: 0 to 4 weeks (MA) | — | |
| 37 | Huq, et al., 2005 | 1997–2000 | 5 different cities, (Bangladesh) | water temperature, air temperature, water depth, pH, rainfall | Poisson regression | bimonthly | — | — | — | — | — | 0, 2, 6, 4, 8 months (SL) | — | |
| Influenza | 38 | Hu, et al., 2012 | 2009 | Brisbane (Australia) | temperature, rainfall, interaction | Poisson regression, spatiotemporal analysis (CAR) | weekly | sinusoidal terms | — | socio-economic index, population (offset), spatially structured random effect | — | AR(1) included | 1 week single lag (SL) | — |
| 39 | Jusot, et al., 2011 | 2009–2010 | Niger | temperature, relative humidity (RH), wind speed, visibility | GAM | daily | seasonal components | trend components | day of the week, holidays, religious festival, and pilgrimage | — | — | — | — | |
Blanks represent unknown for the case no statements are made in articles regarding each category. Otherwise whether it was considered or how it was considered are stated in this table.
* SL: single lag, MA: moving average, DL: distribute lag, AR: auto-regressive term