Krti studies marine diseases via machine learning techniques, and is interested in long-term marine disease implications for planetary health and environmental justice.

All Publications

  • The role of diseases in unifying the health of global estuaries FRONTIERS IN MARINE SCIENCE Tallam, K., White, E. 2023; 10
  • Application of Deep Learning for Classification of Intertidal Eelgrass from Drone-Acquired Imagery REMOTE SENSING Tallam, K., Nguyen, N., Ventura, J., Fricker, A., Calhoun, S., O'Leary, J., Fitzgibbons, M., Robbins, I., Walter, R. K. 2023; 15 (9)

    View details for DOI 10.3390/rs15092321

    View details for Web of Science ID 000987830900001

  • Climatic, land-use and socio-economic factors can predict malaria dynamics at fine spatial scales relevant to local health actors: Evidence from rural Madagascar. PLOS global public health Pourtois, J. D., Tallam, K., Jones, I., Hyde, E., Chamberlin, A. J., Evans, M. V., Ihantamalala, F. A., Cordier, L. F., Razafinjato, B. R., Rakotonanahary, R. J., Tsirinomen'ny Aina, A., Soloniaina, P., Raholiarimanana, S. H., Razafinjato, C., Bonds, M. H., De Leo, G. A., Sokolow, S. H., Garchitorena, A. 2023; 3 (2): e0001607


    While much progress has been achieved over the last decades, malaria surveillance and control remain a challenge in countries with limited health care access and resources. High-resolution predictions of malaria incidence using routine surveillance data could represent a powerful tool to health practitioners by targeting malaria control activities where and when they are most needed. Here, we investigate the predictors of spatio-temporal malaria dynamics in rural Madagascar, estimated from facility-based passive surveillance data. Specifically, this study integrates climate, land-use, and representative household survey data to explain and predict malaria dynamics at a high spatial resolution (i.e., by Fokontany, a cluster of villages) relevant to health care practitioners. Combining generalized linear mixed models (GLMM) and path analyses, we found that socio-economic, land use and climatic variables are all important predictors of monthly malaria incidence at fine spatial scales, via both direct and indirect effects. In addition, out-of-sample predictions from our model were able to identify 58% of the Fokontany in the top quintile for malaria incidence and account for 77% of the variation in the Fokontany incidence rank. These results suggest that it is possible to build a predictive framework using environmental and social predictors that can be complementary to standard surveillance systems and help inform control strategies by field actors at local scales.

    View details for DOI 10.1371/journal.pgph.0001607

    View details for PubMedID 36963091

    View details for PubMedCentralID PMC10021226

  • Predicting Flood Hazards in the Vietnam Central Region: An Artificial Neural Network Approach SUSTAINABILITY Minh Pham Quang, Tallam, K. 2022; 14 (19)
  • IDENTIFICATION OF SNAILS AND SCHISTOSOMA OF MEDICAL IMPORTANCE VIA CONVOLUTIONAL NEURAL NETWORKS Tallam, K., Liu, Z. Y., Chamberlin, A. J., Jones, I. J., Shome, P., Riveau, G., Ndione, R. A., Bandagny, L., Jouanard, N., Eck, P. V., Ngo, T., Sokolow, S. H., De Leo, G. A. AMER SOC TROP MED & HYGIENE. 2021: 295