1. Influence of Climatic Factors on the Incidence of Meningococcal and Pneumococcal Meningitis in Northern Ghana Abudulai Forgor 1, Penelope Vounatsou 2, Tom Smith 2 1 Navrongo Health Research Centre, Ghana 2 Swiss Tropical Institute, Switzerland

2. Study area The Kassena-Nankana District (KND) of northern Ghana lies in the African “meningitis belt”. Epidemics every 8 to 12 yrs for the last 100 yrs. In 1997 epidemic 18,799 cases, 1352 deaths. Climate Rainy season (June – October), dry season (November-May) Dusts blows from Sahara (January-April). Daily temperature is ~40 o C (March-April) Population 140‘000 rural farmers 20‘000 in Navrongo town Study site

3. Main study Longitudinal study between Feb 98- Nov 05 to determine how levels of colonisation with different bacterial serogroups change over time and how the pattern of disease relate to such changes. Colonisation isolates Throat swabs were taken twice annualy from all inhabitants (~300) of 37 randomly selected compounds. From positive plates 2 colonies with neisserial morphology were subcultured Leimkugel et al (2007). PLOS Medicine Waves of colonization and disease in KND (Apr 98-Nov 05) Compound

4. Main study Disease isolates Suspected patients at all health centres of KND were recruited throughout the study. A lumbar puncture was performed and cerebrospinal fluid was analysed Characterisation of Bacterial isolates Meningococci were serogrouped. Multilocus sequence typing was performed. Age and sex patterns of colonisation and disease

5. Climate study Objective To identify environmental factors that can be best used to predict the timing of both meningococcal (MCM) and pneumococcal (PCM) outbreaks Epidemiological data Confirmed cases by CSF with N. meningitidis or S. Pneumoniae (Jan 98-Dec 04). Demographic data available. Meteorological data Weekly means of relative humidity, max, min temperature, wind speed and total rainfall provided by the Navrongo meteo station Total rainfall & humidity

6. Climate study Statistical analysis Negative binomial models (NB) to relate incidence rates of MCM and PCM cases at weekly and monthly intervals with meteo data. Akaike‘s criterion to determine the best lag time for each factor. Bayesian multiple NB models with AR(1) temporal random effects were fitted. Min temperature, relative humidity and number of reported MCM cases in the KND, Reported cases of pneumococcal, meningococcal meningitis cases and max temperature

7. Climate study Results 474 cases of bacterial meningites (SP: 145, Nm: 329) Outbreaks in PCM started earlier than MCM. Concurrent weekly increase in maximum temperature and decrease in total rainfall influence the incidence of MCM Concurrent weekly decrease in total rainfall influences the incidence of PCM. The duration of preceding absence of rainfall is the best predictor of MCM and PCM incidence. Reported pneumococcal, meningococcal meningitis cases and dust Wind speed, relative humidity and pneumococcal meningitis cases

8. Acknowledgements Navrongo Health Research Center Abraham Hodgson Elizabeth Awine Martin Adjuik Swiss Tropical Institute Gerd Pluschke Julia Leimkugel Sebastian Gagneux Valentin Pflüger Jean-Pierre Dagny