Browsing by Author "Clemen, Thomas"

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Developing an Agent-based Model for Evaluating the Effectiveness of Malaria Interventions in Nanyumbu and Masasi Districts, Tanzania
(creative common, 2024-11-28) Mfala, Celina; Nyambo, Devotha; Mwaiswelo, Richard; Mmbando, Bruno; Clemen, Thomas
Objective : Agent-based models and simulation (ABMS) can be utilized to understand the dynamism of transmission and the effect of interventions. We evaluated using ABMS, the efficacy of insecticide-treated bednets (ITNs) at different coverage levels and quality of houses for control of malaria in Masasi and Nanyumbu districts, Tanzania. Methods: The model was developed and simulated in Anylogic software with mosquitoes, humans, and the environment along with their attributes as agents. Using field data, buildings of different qualities were created to be human environment, and ITN use was assigned to respective human agents. Shapefiles were imported into the built-in global imaging system map in Anylogic for better placement of buildings using their coordinates, and coordinates of streams extracted from the study area map were used to allocate the aquatic environment of the mosquito agents. ITNs coverage scenarios of 16%, 40%, 64%, and 80% were simulated. The model was simulated for 90-day period and a model time-step was set to a day. The primary outcome was the prevalence of human agents with malaria infection at the end of the 90-day simulation period. Results: At the end of the 90-day simulation period and initial ITNs coverage of 16% (257/1607), the prevalence of malaria infection was 15.4% (248/1607). When the coverage was increased to 40%, 64%, and 80% malaria prevalence declined to 15.1% (242/1607), 14.1% (227/1607), and 13.9% (223/1607), respectively. ABMS clearly indicated that an increase in ITNs coverage was associated with a decline in the prevalence of infected humans and mosquito population in consistency with the field data. Novelty: This work is unique in a sense that it incorporated the data on house quality which has direct impact in malaria transmission.
Integrated rapid risk assessment for dengue fever in settings with limited diagnostic capacity and uncertain exposure: Development of a methodological framework for Tanzania
(Public Library of Science, 2025-03-28) Belau, Matthias; Boenecke, Juliane; Ströbele, Jonathan; Himmel, Mirko; Dretvić, Daria; Mustafa, Ummul-Khair; Kreppel, Katharina; Sauli, Elingarami; Brinkel, Johanna; Clemen, Ulfia; Clemen, Thomas; Streit, Wolfgang; May, Jürgen; Ahmad, Amena; Reintjes, Ralf; Becher, Heiko
Background Dengue fever is one of the world’s most important re-emerging but neglected infectious diseases. We aimed to develop and evaluate an integrated risk assessment framework to enhance early detection and risk assessment of potential dengue outbreaks in settings with limited routine surveillance and diagnostic capacity. Methods Our risk assessment framework utilizes the combination of various methodological components: We first focused on (I) identifying relevant clinical signals based on a case definition for suspected dengue, (II) refining the signal for potential dengue diagnosis using contextual data, and (III) determining the public health risk associated with a verified dengue signal across various hazard, exposure, and contextual indicators. We then evaluated our framework using (i) historical clinical signals with syndromic and laboratory-confirmed disease information derived from WHO’s Epidemic Intelligence from Open Sources (EIOS) technology using decision tree analyses, and (ii) historical dengue outbreak data from Tanzania at the regional level from 2019 (6,795 confirmed cases) using negative binomial regression analyses adjusted for month and region. Finally, we evaluated a test signal across all steps of our integrated framework to demonstrate the implementation of our multi-method approach. Results The result of the suspected case refinement algorithm for clinically defined syndromic cases was consistent with the laboratory-confirmed diagnosis (dengue yes or no). Regression between confirmed dengue fever cases in 2019 as the dependent variable and a site-specific public health risk score as the independent variable showed strong evidence of an increase in dengue fever cases with higher site-specific risk (rate ratio = 2.51 (95% CI = [1.76, 3.58])). Conclusions The framework can be used to rapidly determine the public health risk of dengue outbreaks, which is useful for planning and prioritizing interventions or for epidemic preparedness. It further allows for flexibility in its adaptation to target diseases and geographical contexts.
Leveraging peer-to-peer farmer learning to facilitate better strategies in smallholder dairy husbandry
(SAGE journal, 2020-12-02) Nyambo, Devotha G.; Luhanga, Edith T.; Yonah, Zaipuna O.; Mujibi, Fidalis D. N; Clemen, Thomas
Peer-to-peer learning paradigm is seldom used in studying how farmers can increase yield. In this article, agent-based modelling has been applied to study the chances of dairy farmers increasing annual milk yield by learning better farming strategies from each other. Two sets of strategies were considered; in one set (S), each farmer agent would possess a number of farming strategies based on their knowledge, and in a second set (S'), farmer agents would possess farming strategies that they have adopted from their peers. Regression models were used to determine litres of milk that could be produced whenever new strategies were applied. By using data from Ethiopia and Tanzania, 28 and 25 determinants for increase in milk yield were fitted for the two countries, respectively. There was a significant increase in average milk yield as the farmer agents interacted and updated their S'– from baseline data, average milk yield of 12.7 ± 4.89 and 13.62 ± 4.47 to simulated milk yield average of 17.57 ± 0.72 and 20.34 ± 1.16 for Tanzania and Ethiopia, respectively. The peer-to-peer learning approach details an inexpensive method manageable by the farmers themselves. Its implementation could range from physical farmer groups to online interactions.