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2. Mapping refugee populations at high resolution by unlocking humanitarian administrative data

Author

Edith Darin, Ahmadou Hamady Dicko, Hisham Galal, Rebeca Moreno Jimenez, Hyunju Park, Andrew J. Tatem, and Sarchil Qader

Subjects
Gridded population, Refugee, Administrative data, Building footprint, Satellite imagery, Anthropology, GN1-890, International relations, JZ2-6530
Abstract

Background Informing local decision-making, improving service delivery and designing household surveys require having access to high-spatial resolution mapping of the targeted population. However, this detailed spatial information remains unavailable for specific population subgroups, such as refugees, a vulnerable group that would significantly benefit from focused interventions. Given the continuous increase in the number of refugees, reaching an all-time high of 35.3 million people in 2022, it is imperative to develop models that can accurately inform about their spatial locations, enabling better and more tailored assistance. Methods We leverage routinely collected registration data on refugees and combine it with high-resolution population maps, satellite imagery derived settlement maps and other spatial covariates to disaggregate observed refugee totals into 100-m grid cells. We suggest a deterministic grid cell allocation inside monitored refugee sites based on building count and a random-forest-derived grid cell allocation outside refugee sites based on geolocating the textual geographic information in the refugee register and on high-resolution population mapping. We test the method in Cameroon using the registration database monitored by the United Nations High Commissioner for Refugees. Results Using OpenStreetMap, 83% of the manually inputted information in the registration database could be geolocated. The building footprint layer derived from satellite imagery by Ecopia AI offers extensive coverage within monitored refugee sites, although manual digitization was still required in rapidly evolving settings. The high-resolution mapping of refugees on a 100-m grid basis provides an unparalleled level of spatial detail, enabling valuable geospatial insights for informed local decision-making. Conclusions Gathering information on forcibly displaced persons in sparse data-setting environment can quickly become very costly. Therefore, it is critical to gain the most knowledge from operational data that is frequently collected, such as registration databases. Integrating it with ancillary information derived from satellite imagery paves the way for obtaining more timely and spatially precise information to better deliver services and enhance sampling frame for target data collection exercises that further improves the quality of information on people in need.

Published
2024
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4. Global-scale evaluation of precipitation datasets for hydrological modelling

Author

S. H. Gebrechorkos, J. Leyland, S. J. Dadson, S. Cohen, L. Slater, M. Wortmann, P. J. Ashworth, G. L. Bennett, R. Boothroyd, H. Cloke, P. Delorme, H. Griffith, R. Hardy, L. Hawker, S. McLelland, J. Neal, A. Nicholas, A. J. Tatem, E. Vahidi, Y. Liu, J. Sheffield, D. R. Parsons, and S. E. Darby

Subjects
Technology, Environmental technology. Sanitary engineering, TD1-1066, Geography. Anthropology. Recreation, Environmental sciences, GE1-350
Abstract

Precipitation is the most important driver of the hydrological cycle, but it is challenging to estimate it over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR, hereafter PERCCDR) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against 1825 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly, and daily timescales, MSWEP followed by ERA5 demonstrated a higher correlation (CC) and Kling–Gupta efficiency (KGE) than other datasets for more than 50 % of the stations, whilst ERA5 was the second-highest-performing dataset, and it showed the highest error and bias for about 20 % of the stations. PERCCDR is the least-well-performing dataset, with a bias of up to 99 % and a normalised root mean square error of up to 247 %. PERCCDR only show a higher KGE and CC than the other products for less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region, or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products.

Published
2024
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5. Bayesian hierarchical modelling approaches for combining information from multiple data sources to produce annual estimates of national immunization coverage

Author

Utazi, C. Edson, Jochem, Warren C., Gacic-Dobo, Marta, Murphy, Padraic, Sahu, Sujit K., Danovaro-Holliday, Carolina M., and Tatem, Andrew J.

Subjects
Statistics - Methodology
Abstract

Estimates of national immunization coverage are crucial for guiding policy and decision-making in national immunization programs and setting the global immunization agenda. WHO and UNICEF estimates of national immunization coverage (WUENIC) are produced annually for various vaccine-dose combinations and all WHO Member States using information from multiple data sources and a deterministic computational logic approach. This approach, however, is incapable of characterizing the uncertainties inherent in coverage measurement and estimation. It also provides no statistically principled way of exploiting and accounting for the interdependence in immunization coverage data collected for multiple vaccines, countries and time points. Here, we develop Bayesian hierarchical modeling approaches for producing accurate estimates of national immunization coverage and their associated uncertainties. We propose and explore two candidate models: a balanced data single likelihood (BDSL) model and an irregular data multiple likelihood (IDML) model, both of which differ in their handling of missing data and characterization of the uncertainties associated with the multiple input data sources. We provide a simulation study that demonstrates a high degree of accuracy of the estimates produced by the proposed models, and which also shows that the IDML model is the better model. We apply the methodology to produce coverage estimates for select vaccine-dose combinations for the period 2000-2019. A contributed R package {\tt imcover} implementing the No-U-Turn Sampler (NUTS) in the Stan programming language enhances the utility and reproducibility of the methodology., Comment: 31 pages (main), 4 figures

Published
2022

6. COVID-19 Lessons Learned, Lessons Unlearned, Lessons for the Future

Author

Hollenberg, Steven M, Janz, David R, Hua, May, Malesker, Mark, Qadir, Nida, Rochwerg, Bram, Sessler, Curtis N, Tatem, Geneva, Rice, Todd W, Board, CHEST Critical Care Editorial, Ginde, Adit A, Kerlin, Meeta P, Lilly, Craig M, and Summers, Charlotte

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Good Health and Well Being, Humans, COVID-19, Pandemics, Thorax, Critical Care, Brachytherapy, CHEST Critical Care Editorial Board, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

The COVID-19 pandemic has affected clinicians in many different ways. Clinicians have their own experiences and lessons that they have learned from their work in the pandemic. This article outlines a few lessons learned from the eyes of CHEST Critical Care Editorial Board members, namely practices which will be abandoned, novel practices to be adopted moving forward, and proposed changes to the health care system in general. In an attempt to start the discussion of how health care can grow from the pandemic, the editorial board members outline their thoughts on these lessons learned.

Published
2022

7. Optimizing the detection of emerging infections using mobility-based spatial sampling

Author

Die Zhang, Yong Ge, Jianghao Wang, Haiyan Liu, Wen-Bin Zhang, Xilin Wu, Gerard B. M. Heuvelink, Chaoyang Wu, Juan Yang, Nick W. Ruktanonchai, Sarchil H. Qader, Corrine W. Ruktanonchai, Eimear Cleary, Yongcheng Yao, Jian Liu, Chibuzor C. Nnanatu, Amy Wesolowski, Derek A.T. Cummings, Andrew J. Tatem, and Shengjie Lai

Subjects
Human mobility, Data analysis, Spatial sampling, Testing allocation, Emerging infectious disease, Physical geography, GB3-5030, Environmental sciences, GE1-350
Abstract

Timely and precise detection of emerging infections is imperative for effective outbreak management and disease control. Human mobility significantly influences the spatial transmission dynamics of infectious diseases. Spatial sampling, integrating the spatial structure of the target, holds promise as an approach for testing allocation in detecting infections, and leveraging information on individuals’ movement and contact behavior can enhance targeting precision. This study introduces a spatial sampling framework informed by spatiotemporal analysis of human mobility data, aiming to optimize the allocation of testing resources for detecting emerging infections. Mobility patterns, derived from clustering point-of-interest and travel data, are integrated into four spatial sampling approaches at the community level. We evaluate the proposed mobility-based spatial sampling by analyzing both actual and simulated outbreaks, considering scenarios of transmissibility, intervention timing, and population density in cities. Results indicate that leveraging inter-community movement data and initial case locations, the proposed Case Flow Intensity (CFI) and Case Transmission Intensity (CTI)-informed spatial sampling enhances community-level testing efficiency by reducing the number of individuals screened while maintaining a high accuracy rate in infection identification. Furthermore, the prompt application of CFI and CTI within cities is crucial for effective detection, especially in highly contagious infections within densely populated areas. With the widespread use of human mobility data for infectious disease responses, the proposed theoretical framework extends spatiotemporal data analysis of mobility patterns into spatial sampling, providing a cost-effective solution to optimize testing resource deployment for containing emerging infectious diseases.

Published
2024
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14. Early trajectories of virological and immunological biomarkers and clinical outcomes in patients admitted to hospital for COVID-19: an international, prospective cohort study

Author

Sahner, David, Tierney, John, Vogel, Susan E., Herpin, Betsey R., Smolskis, Mary C., McKay, Laura A., Cahill, Kelly, Crew, Page, Sardana, Ratna, Raim, Sharon Segal, Hensely, Lisa, Lorenzo, Johsua, Mock, Rebecca, Zuckerman, Judith, Atri, Negin, Miller, Mark, Vallee, David, Chung, Lucy, Kang, Nayon, Barrett, Kevin, Adam, Stacey J., Read, Sarah, Draghia-Akli, Ruxandra, Currier, Judy, Hughes, Eric, Harrigan, Rachel H., Amos, Laura, Carlsen, Amy, Carter, Anita, Collins, Gary, Davis, Bionca, Denning, Eileen, DuChene, Alain, Eckroth, Kate, Engen, Nicole, Frase, Alex, Gandits, Greg, Grund, Birgit, Harrison, Merrie, Hurlbut, Nancy, Kaiser, Payton, Koopmeiners, Joseph, Larson, Gregg, Meger, Sue, Mistry, Shweta Sharma, Murray, Thomas, Nelson, Ray, Quan, Kien, Quan, Siu Fun, Reilly, Cavan, Siegel, Lianne, Thompson, Greg, Vock, David, Walski, Jamie, Gelijns, Annetine C., Moskowitz, Alan J., Bagiella, Emilia, Moquete, Ellen, O'Sullivan, Karen, Marks, Mary E., Accardi, Evan, Kinzel, Emily, Burris, Sarah, Bedoya, Gabriela, Gupta, Lola, Overbey, Jessica R., Santos, Milerva, Gillinov, Marc A., Miller, Marissa A., Taddei-Peters, Wendy C., Fenton, Kathleen, Sandkovsky, Uriel, Gottlieb, Robert L., Mack, Michael, Berhe, Mezgebe, Haley, Clinton, Dishner, Emma, Bettacchi, Christopher, Golden, Kevin, Duhaime, Erin, Ryan, Madison, Tallmadge, Catherine, Estrada, Lorie, Jones, Felecia, Villa, Samatha, Wang, Samatha, Robert, Raven, Coleman, Tanquinisha, Clariday, Laura, Baker, Rebecca, Hurutado-Rodriguez, Mariana, Iram, Nazia, Fresnedo, Michelle, Davis, Allyson, Leonard, Kiara, Ramierez, Noelia, Thammavong, Jon, Duque, Krizia, Turner, Emma, Fisher, Tammy, Robinson, Dianna, Ransom, Desirae, Maldonado, Nicholas, Lusk, Erica, Killian, Aaron, Palacious, Adriana, Solis, Edilia, Jerrow, Janet, Watts, Matthew, Whitacre, Heather, Cothran, Elizabeth, Smith, Peter K., Barkauskas, Christina E., Vekstein, Andrew M., Ko, Emily R., Dreyer, Grace R., Stafford, Neil, Brooks, Megan, Der, Tatyana, Witte, Marie, Gamarallage, Ruwan, Franzone, John, Ivey, Noel, Lumsden, Rebecca H., Mosaly, Nilima, Mourad, Ahmaad, Holland, Thomas L., Motta, Mary, Lane, Kathleen, McGowan, Lauren M., Stout, Jennifer, Aloor, Heather, Bragg, Kennesha M., Toledo, Barvina, McLendon-Arvik, Beth, Bussadori, Barbara, Hollister, Beth A., Griffin, Michelle, Giangiacomo, Dana M., Rodriguez, Vicente, Bokhart, Gordon, Eichman, Sharon M., Parrino, Patrick E., Spindel, Stephen, Bansal, Aditya, Baumgarten, Katherine, Hand, Johnathan, Vonderhaar, Derek, Nossaman, Bobby, Sylvia Laudun, Ames, DeAnna, Broussard, Shane, Hernandez, Nilmo, Isaac, Geralyn, Dinh, Huan, Zheng, Yiling, Tran, Sonny, McDaniel, Hunter, Crovetto, Nicolle, Perin, Emerson, Costello, Briana, Manian, Prasad, Sohail, M. Rizwan, Postalian, Alexander, Hinsu, Punit, Watson, Carolyn, Chen, James, Fink, Melyssa, Sturgis, Lydia, Walker, Kim, Mahon, Kim, Parenti, Jennifer, Kappenman, Casey, Knight, Aryn, Sturek, Jeffrey M., Barros, Andrew, Enfield, Kyle B., Kadl, Alexandra, Green, China J., Simon, Rachel M., Fox, Ashley, Thornton, Kara, Adams, Amy, Badhwar, Vinay, Sharma, Sunil, Peppers, Briana, McCarthy, Paul, Krupica, Troy, Sarwari, Arif, Reece, Rebecca, Fornaresico, Lisa, Glaze, Chad, Evans, Raquel, Di, Fang, Carlson, Shawn, Aucremanne, Tanja, Tennant, Connie, Sutton, Lisa Giblin, Buterbaugh, Sabrina, Williams, Roger, Bunner, Robin, Traverse, Jay H., Rhame, Frank, Huelster, Joshua, Kethireddy, Rajesh, Davies, Irena, Salamanca, Julianne, Majeski, Christine, Skelton, Paige, Zarambo, Maria, Sarafolean, Andrea, Bowdish, Michael E., Borok, Zea, Wald-Dickler, Noah, Hutcheon, Douglass, Towfighi, Amytis, Lee, Mary, Lewis, Meghan R., Spellberg, Brad, Sher, Linda, Sharma, Aniket, Olds, Anna P., Justino, Chris, Loxano, Edward, Romero, Chris, Leong, Janet, Rodina, Valentina, Quesada, Christine, Hamilton, Luke, Escobar, Jose, Leshnower, Brad, Bender, William, Sharifpour, Milad, Miller, Jeffrey, Farrington, Woodrow, Baio, Kim T., McBride, Mary, Fielding, Michele, Mathewson, Sonya, Porte, Kristina, Maton, Missy, Ponder, Chari, Haley, Elisabeth, Spainhour, Christine, Rogers, Susan, Tyler, Derrick, Madathil, Ronson J., Rabin, Joseph, Levine, Andrea, Saharia, Kapil, Tabatabai, Ali, Lau, Christine, Gammie, James S., Peguero, Maya-Loren, McKernan, Kimberly, Audette, Mathew, Fleischmann, Emily, Akbari, Kreshta, Lee, Myounghee, Chi, Andrew, Salehi, Hanna, Pariser, Alan, Nyguyen, Phuong Tran, Moore, Jessica, Gee, Adrienne, Vincent, Shelika, Zuckerman, Richard A., Iribarne, Alexander, Metzler, Sara, Shipman, Samantha, Johnson, Haley, Newton, Crystallee, Parr, Doug, Miller, Leslie, Schelle, Beth, McLean, Sherry, Rothbaum, Howard R., Alvarez, Michael S., Kalan, Shivam P., Germann, Heather H., Hendershot, Jennifer, Moroney, Karen, Herring, Karen, Cook, Sharri, Paul, Pam, Walker-Ignasiak, Rebecca, North, Crystal, Oldmixon, Cathryn, Ringwood, Nancy, Muzikansky, Ariela, Morse, Richard, Fitzgerald, Laura, Morin, Haley D., Brower, Roy G., Reineck, Lora A., Bienstock, Karen, Steingrub, Jay H., Hou, Peter K., Steingrub, Jay S., Tidswell, Mark A., Kozikowski, Lori-Ann, Kardos, Cynthia, DeSouza, Leslie, Romain, Sarah, Thornton-Thompson, Sherell, Talmor, Daniel, Shapiro, Nathan, Andromidas, Konstantinos, Banner-Goodspeed, Valerie, Bolstad, Michael, Boyle, Katherine L., Cabrera, Payton, deVilla, Arnaldo, Ellis, Joshua C., Grafals, Ana, Hayes, Sharon, Higgins, Conor, Kurt, Lisa, Kurtzman, Nicholas, Redman, Kimberly, Rosseto, Elinita, Scaffidi, Douglas, Filbin, Michael R., Hibbert, Kathryn A., Parry, Blair, Margolin, Justin, Hillis, Brooklynn, Hamer, Rhonda, Brait, Kelsey, Beakes, Caroline, McKaig, Brenna, Kugener, Eleonore, Jones, Alan E., Galbraith, James, Nandi, Utsav, Peacock, Rebekah, Hendey, Gregory, Kangelaris, Kirsten, Ashktorab, Kimia, Gropper, Rachel, Agrawal, Anika, Yee, Kimberley J., Jauregui, Alejandra E., Zhuo, Hanjing, Almasri, Eyad, Fayed, Mohamed, Hubel, Kinsley A., Hughes, Alyssa R., Garcia, Rebekah L., Lim, George W., Chang, Steven Y., Lin, Michael Y., Vargas, Julia, Sihota, Hena, Beutler, Rebecca, Agarwal, Trisha, Wilson, Jennifer G., Vojnik, Rosemary, Perez, Cynthia, McDowell, Jordan H., Roque, Jonasel, Wang, Henry, Huebinger, Ryan M., Patel, Bela, Vidales, Elizabeth, Albertson, Timothy, Hardy, Erin, Harper, Richart, Moss, Marc A., Baduashvili, Amiran, Chauhan, Lakshmi, Douin, David J., Martinez, Flora, Finck, Lani L., Bastman, Jill, Howell, Michelle, Higgins, Carrie, McKeehan, Jeffrey, Finigan, Jay, Stubenrauch, Peter, Janssen, William J., Griesmer, Christine, VerBurg, Olivia, Hyzy, Robert C., Park, Pauline K., Nelson, Kristine, McSparron, Jake I., Co, Ivan N., Wang, Bonnie R., Jimenez, Jose, Olbrich, Norman, McDonough, Kelli, Jia, Shijing, Hanna, Sinan, Gong, Michelle N., Richardson, Lynne D., Nair, Rahul, Lopez, Brenda, Amosu, Omowunmi, Offor, Obiageli, Tzehaie, Hiwet, Nkemdirim, William, Boujid, Sabah, Mosier, Jarrod M., Hypes, Cameron, Campbell, Elizabeth Salvagio, Bixby, Billie, Gilson, Boris, Lopez, Anitza, Bime, Christian, Parthasarathy, Sairam, Cano, Ariana M., Hite, R. Duncan, Terndrup, Thomas E., Wiedemann, Herbert P., Hudock, Kristin, Tanzeem, Hammad, More, Harshada, Martinkovic, Jamie, Sellers, Susan, Houston, Judy, Burns, Mary, Kiran, Simra, Roads, Tammy, Kennedy, Sarah, Duggal, Abhijit, Thiruchelvam, Nirosshan, Ashok, Kiran, King, Alexander H., Mehkri, Omar, Dugar, Siddharth, Sahoo, Debasis, Yealy, Donald M., Angus, Derek C., Weissman, Alexandra J., Vita, Tina M., Berryman, Emily, Hough, Catherine L., Khan, Akram, Krol, Olivia F., Mills, Emmanuel, Kinjal, Mistry, Briceno, Genesis, Reddy, Raju, Hubel, Kinsley, Jouzestani, Milad K., McDougal, Madeline, Deshmukh, Rupali, Johnston, Nicholas J., Robinson, Bryce H., Gundel, Staphanie J., Katsandres, Sarah C., Chen, Peter, Torbati, Sam S., Parimon, Tanyalak, Caudill, Antonina, Mattison, Brittany, Jackman, Susan E., Chen, Po-En, Bayoumi, Emad, Ojukwu, Cristabelle, Fine, Devin, Weissberg, Gwendolyn, Isip, Katherine, Choi-Kuaea, Yunhee, Mehdikhani, Shaunt, Dar, Tahir B., Fleury Augustin, Nsole Biteghe, Tran, Dana, Dukov, Jennifer Emilow, Matusov, Yuri, Choe, June, Hindoyan, Niree A., Wynter, Timothy, Pascual, Ethan, Clapham, Gregg J., Herrera, Lisa, Caudill, Antonia, O’Mahony, D. Shane, Nyatsatsang, Sonam T., Wilson, David M., Wallick, Julie A., Duven, Alexandria M., Fletcher, Dakota D., Miller, Chadwick, Files, D. Clark, Gibbs, Kevin W., Flores, Lori S., LaRose, Mary E., Landreth, Leigha D., Palacios, D. Rafael, Parks, Lisa, Hicks, Madeline, Goodwin, Andrew J., Kilb, Edward F., Lematty, Caitlan T., Patti, Kerilyn, Grady, Abigail, Rasberry, April, Morris, Peter E., Sturgill, Jamie L., Cassity, Evan P., Dhar, Sanjay, Montgomery-Yates, Ashley A., Pasha, Sarah N., Mayer, Kirby P., Pharm.D., Brittany Bissel, Trott, Terren, Rehman, Shahnaz, de Wit, Marjolein, Mason, Jessica, Bledsoe, Joseph, Knowlton, Kirk U., Brown, Samuel, Lanspa, Michael, Leither, Lindsey, Pelton, Ithan, Armbruster, Brent P., Montgomery, Quinn, Kumar, Naresh, Fergus, Melissa, Imel, Karah, Palmer, Ghazal, Webb, Brandon, Klippel, Carolyn, Jensen, Hannah, Duckworth, Sarah, Gray, Andrew, Burke, Tyler, Knox, Dan, Lumpkin, Jenna, Aston, Valerie T., Applegate, Darrin, Serezlic, Erna, Brown, Katie, Merril, Mardee, Harris, Estelle S., Middleton, Elizabeth A., Barrios, Macy A.G., Greer, Jorden, Schmidt, Amber D., Webb, Melissa K., Paine, Roert, Callahan, Sean J., Waddoups, Lindsey J., Yamane, Misty B., Self, Wesley H., Rice, Todd W., Casey, Jonathan D., Johnson, Jakea, Gray, Christopher, Hays, Margaret, Roth, Megan, Menon, Vidya, Kasubhai, Moiz, Pillai, Anjana, Daniel, Jean, Sittler, Daniel, Kanna, Balavenkatesh, Jilani, Nargis, Amaro, Francisco, Santana, Jessica, Lyakovestsky, Aleksandr, Madhoun, Issa, Desroches, Louis Marie, Amadon, Nicole, Bahr, Alaa, Ezzat, Imaan, Guerrero, Maryanne, Padilla, Joane, Fullmer, Jessie, Singh, Inderpreet, Ali Shah, Syed Hamad, Narang, Rajeev, Mock, Polly, Shadle, Melissa, Hernandez, Brenda, Welch, Kevin, Payne, Andrea, Ertl, Gabriela, Canario, Daniel, Barrientos, Isabel, Goss, Danielle, DeVries, Mattie, Folowosele, Ibidolapo, Garner, Dorothy, Gomez, Mariana, Price, Justin, Bansal, Ekta, Wong, Jim, Faulhaber, Jason, Fazili, Tasaduq, Yeary, Brian, Ndolo, Ruth, Bryant, Christina, Smigeil, Bridgette, Robinson, Philip, Najjar, Rana, Jones, Patrice, Nguyen, Julie, Chin, Christina, Taha, Hassan, Najm, Salah, Smith, Christopher, Moore, Jason, Nassar, Talal, Gallinger, Nick, Christian, Amy, Mauer, D’Amber, Phipps, Ashley, Waters, Michael, Zepeda, Karla, Coslet, Jordan, Landazuri, Rosalynn, Pineda, Jacob, Uribe, Nicole, Garcia, Jose Ruiz, Barbabosa, Cecilia, Sandler, Kaitlyn, Overcash, J. Scott, Marquez, Adrienna, Chu, Hanh, Lee, Kia, Quillin, Kimberly, Garcia, Andrea, Lew, Pauline, Rogers, Ralph, Shehadeh, Fadi, Mylona, Evangelia K., Kaczynski, Matthew, Tran, Quynh-Lam, Benitez, Gregorio, Mishra, Biswajit, Felix, Lewis Oscar, Vafea, Maria Tsikala, Atalla, Eleftheria, Davies, Robin, Hedili, Salma, Monkeberg, Maria Andrea, Tabler, Sandra, Harrington, Britt, Meegada, Sreenath, Koripalli, Venkata Sandeep, Muddana, Prithvi, Jain, Lakshay, Undavalli, Chaitanya, Kavya, Parasa, Ibiwoye, Mofoluwaso, Akilo, Hameed, Lovette, Bryce D., Wylie, Jamie-Crystal, Smith, Diana M., Poon, Kenneth, Eckardt, Paula, Heysu, Rubio-Gomez, Sundararaman, Nithya, Alaby, Doris, Sareli, Candice, Sánchez, Adriana, Popielski, Laura, Kambo, Amy, Viens, Kimberley, Turner, Melissa, Vjecha, Michael J., Weintrob, Amy, Brar, Indira, Markowitz, Norman, Pastor, Erika, Corpuz, Roweena, Alangaden, George, McKinnon, John, Ramesh, Mayur, Herc, Erica, Yared, Nicholas, Lanfranco, Odaliz Abreu, Rivers, Emanuel, Swiderek, Jennifer, Gupta, Ariella Hodari, Pabla, Pardeep, Eliya, Sonia, Jazrawi, Jehan, Delor, Jeremy, Desai, Mona, Cook, Aaron, Jaehne, Anja Kathrina, Gill, Jasreen Kaur, Renaud, Sheri, Sarveswaran, Siva, Gardner, Edward, Scott, James, Bianchini, Monica, Melvin, Casey, Kim, Gina, Wyles, David, Kamis, Kevin, Miller, Rachel, Douglas, Ivor, Haukoos, Jason, Hicks, Carrie, Lazarte, Susana, Marines-Price, Rubria, Osuji, Alice, Agbor Agbor, Barbine Tchamba, Petersen, Tianna, Kamel, Dena, Hansen, Laura, Garcia, Angie, Cha, Christine, Mozaffari, Azadeh, Hernandez, Rosa, Cutrell, James, Kim, Mina, DellaValle, Natalie, Gonzales, Sonia, Somboonwit, Charurut, Oxner, Asa, Guerra, Lucy, Hayes, Michael, Nguyen, Thi, Tran, Thanh, Pinto, Avenette, Hatlen, Timothy, Anderson, Betty, Zepeda-Gutierrez, Ana, Martin, Dannae, Temblador, Cindi, Cuenca, Avon, Tanoviceanu, Roxanne, Prieto, Martha, Guerrero, Mario, Daar, Eric, Correa, Ramiro, Hartnell, Gabe, Wortmann, Glenn, Doshi, Saumil, Moriarty, Theresa, Gonzales, Melissa, Garman, Kristin, Baker, Jason V., Frosch, Anne, Goldsmith, Rachael, Driver, Brian, Frank, Christine, Leviton, Tzivia, Prekker, Matthew, Jibrell, Hodan, Lo, Melanie, Klaphake, Jonathan, Mackedanz, Shari, Ngo, Linh, Garcia-Myers, Kelly, Kunisaki, Ken M., Wendt, Chris, Melzer, Anne, Wetherbee, Erin, Drekonja, Dimitri, Pragman, Alexa, Hamel, Aimee, Thielen, Abbie, Hassler, Miranda, Walquist, Mary, Augenbraun, Michael, George, Jensen, Demeo, Lynette, Mishko, Motria, Thomas, Lorraine, Tatem, Luis, Dehovitz, Jack, Abassi, Mahsa, Leuck, Anne-Marie, Rao, Via, Pullen, Matthew, Luke, Darlette, LaBar, Derek, Christiansen, Theresa, Howard, Diondra, Biswas, Kousick, Harrington, Cristin, Garcia, Amanda, Bremer, Tammy, Burke, Tara, Koker, Brittany, Davis-Karim, Anne, Pittman, David, Vasudeva, Shikha S., Johnstone, Jaylynn R., Agnetti, Kate, Davis, Ruby, Trautner, Barbara, Hines-Munson, Casey, Van, John, Dillon, Laura, Wang, Yiqun, Nagy-Agren, Stephanie, Vasudeva, Shikha, Ochalek, Tracy, Caldwell, Erin, Humerickhouse, Edward, Boone, David, McGraw, William, Looney, David J., Mehta, Sanjay R., Johns, Scott Thompson, St. John, Melissa, Raceles, Jacqueline, Sear, Emily, Funk, Stephen, Cesarini, Rosa, Fang, Michelle, Nicalo, Keith, Drake, Wonder, Jones, Beatrice, Holtman, Teresa, Nguyen, Hien H., Maniar, Archana, Johnson, Eric A., Nguyen, Lam, Tran, Michelle T., Barrett, Thomas W., Johnston, Tera, Huggins, John T., Beiko, Tatsiana Y., Hughes, Heather Y., McManigle, William C., Tanner, Nichole T., Washburn, Ronald G., Ardelt, Magdalena, Tuohy, Patricia A., Mixson, Jennifer L., Hinton, Charles G., Thornley, Nicola, Allen, Heather, Elam, Shannon, Boatman, Barry, Baber, Brittany J., Ryant, Rudell, Roller, Brentin, Nguyen, Chinh, Mikail, Amani Morgan, Research, Marivic Hansen, Lichtenberger, Paola, Baracco, Gio, Ramos, Carol, Bjork, Lauren, Sueiro, Melyssa, Tien, Phyllis, Freasier, Heather, Buck, Theresa, Nekach, Hafida, Holodniy, Mark, Chary, Aarthi, Lu, Kan, Peters, Theresa, Lopez, Jessica, Tan, Susanna Yu, Lee, Robert H., Asghar, Aliya, Karyn Isip, Tasadduq Karim, Le, Katherine, Nguyen, Thao, Wong, Shinn, Raben, Dorthe, Murray, Daniel D., Jensen, Tomas O., Peters, Lars, Aagaard, Bitten, Nielsen, Charlotte B., Krapp, Katharina, Nykjær, Bente Rosdahl, Olsson, Christina, Kanne, Katja Lisa, Grevsen, Anne Louise, Joensen, Zillah Maria, Bruun, Tina, Bojesen, Ane, Woldbye, Frederik, Normand, Nick E., Esman, Frederik V.L., Benfield, Thomas, Clausen, Clara Lundetoft, Hovmand, Nichlas, Israelsen, Simone Bastrup, Iversen, Katrine, Leding, Caecilie, Pedersen, Karen Brorup, Thorlacius-Ussing, Louise, Tinggaard, Michaela, Tingsgard, Sandra, Krohn-Dehli, Louise, Pedersen, Dorthe, Villadsen, Signe, Staehr Jensen, Jens-Ulrik, Overgaard, Rikke, Rastoder, Ema, Heerfordt, Christian, Hedsund, Caroline, Ronn, Christian Phillip, Kamstrup, Peter Thobias, Hogsberg, Dorthe Sandbaek, Bergsoe, Christina, Søborg, Christian, Hissabu, Nuria M.S., Arp, Bodil C., Ostergaard, Lars, Staerke, Nina Breinholt, Yehdego, Yordanos, Sondergaard, Ane, Johansen, Isik S., Pedersen, Andreas Arnholdt, Knudtzen, Fredrikke C., Larsen, Lykke, Hertz, Mathias A., Fabricius, Thilde, Holden, Inge K., Lindvig, Susan O., Helleberg, Marie, Gerstoft, Jan, Kirk, Ole, Jensen, Tomas Ostergaard, Madsen, Birgitte Lindegaard, Pedersen, Thomas Ingemann, Harboe, Zitta Barrella, Roge, Birgit Thorup, Hansen, Thomas Michael, Glesner, Matilde Kanstrup, Lofberg, Sandra Valborg, Nielsen, Ariella Denize, Leicht von Huth, Sebastian, Nielsen, Henrik, Thisted, Rikke Krog, Petersen, Kristine Toft, Juhl, Maria Ruwald, Podlekareva, Daria, Johnsen, Stine, Andreassen, Helle Frost, Pedersen, Lars, Clara Ellinor Lindnér, Cecilia Ebba, Wiese, Lothar, Knudsen, Lene Surland, Skrøder Nytofte, Nikolaj Julian, Havmøller, Signe Ravn, Expósito, Maria, Badillo, José, Martínez, Ana, Abad, Elena, Chamorro, Ana, Figuerola, Ariadna, Mateu, Lourdes, España, Sergio, Lucero, Maria Constanza, Santos, José Ramón, Lladós, Gemma, Lopez, Cristina, Carabias, Lydia, Molina-Morant, Daniel, Loste, Cora, Bracke, Carmen, Siles, Adrian, Fernández-Cruz, Eduardo, Di Natale, Marisa, Padure, Sergiu, Gomez, Jimena, Ausin, Cristina, Cervilla, Eva, Balastegui, Héctor, Sainz, Carmen Rodríguez, Lopez, Paco, Carbone, Javier, Escobar, Mariam, Balerdi, Leire, Legarda, Almudena, Roldan, Montserrat, Letona, Laura, Muñoz, José, Camprubí, Daniel, Arribas, Jose R., Sánchez, Rocio Montejano, Díaz-Pollán, Beatriz, Stewart, Stefan Mark, Garcia, Irene, Borobia, Alberto, Mora-Rillo, Marta, Estrada, Vicente, Cabello, Noemi, Nuñez-Orantos, M.J., Sagastagoitia, I., Homen, J.R., Orviz, E., Montalvá, Adrián Sánchez, Espinosa-Pereiro, Juan, Bosch-Nicolau, Pau, Salvador, Fernando, Burgos, Joaquin, Morales-Rull, Jose Luis, Moreno Pena, Anna Maria, Acosta, Cristina, Solé-Felip, Cristina, Horcajada, Juan P., Sendra, Elena, Castañeda, Silvia, López-Montesinos, Inmaculada, Gómez-Junyent, Joan, Gonzáles, Carlota Gudiol, Cuervo, Guilermo, Pujol, Miquel, Carratalà, Jordi, Videla, Sebastià, Günthard, Huldrych, Braun, Dominique L., West, Emily, M’Rabeth-Bensalah, Khadija, Eichinger, Mareile L., Grüttner-Durmaz, Manuela, Grube, Christina, Zink, Veronika, pharmacist, Goes pharmacist, Josefine, Fätkenheuer, Gerd, Malin, Jakob J., Tsertsvadze, Tengiz, Abutidze, Akaki, Chkhartishvili, Nikoloz, Metchurtchlishvili, Revaz, Endeladze, Marina, Paciorek, Marcin, Bursa, Dominik, Krogulec, Dominika, Pulik, Piotr, Ignatowska, Anna, Horban, Andrzej, Bakowska, Elzbieta, Kowaska, Justyna, Bednarska, Agnieszka, Jurek, Natalia, Skrzat-Klapaczynska, Agata, Bienkowski, Carlo, Hackiewicz, Malgorzata, Makowiecki, Michal, Platowski, Antoni, Fishchuk, Roman, Kobrynska, Olena, Levandovska, Khrystyna, Kirieieva, Ivanna, Kuziuk, Mykhailo, Naucler, Pontus, Perlhamre, Emma, Mazouch, Lotta, Kelleher, Anthony, Polizzotto, Mark, Carey, Catherine, Chang, Christina C., Hough, Sally, Virachit, Sophie, Davidson, Sarah, Bice, Daniel J., Ognenovska, Katherine, Cabrera, Gesalit, Flynn, Ruth, Young, Barnaby E., Chia, Po Ying, Lee, Tau Hong, Lin, Ray J., Lye, David C., Ong, Sean W.X., Puah, Ser Hon, Yeo, Tsin Wen, Diong, Shiau Hui, Ongko, Juwinda, Yeo, He Ping, Eriobu, Nnakelu, Kwaghe, Vivian, Zaiyad, Habib, Idoko, Godwin, Uche, Blessing, Selvamuthu, Poongulali, Kumarasamy, Nagalingeswaran, Beulah, Faith Ester, Govindarajan, Narayan, Mariyappan, Kowsalya, Losso, Marcelo H., Abela, Cecilia, Moretto, Renzo, Belloc, Carlos G., Ludueña, Jael, Amar, Josefina, Toibaro, Javier, Macias, Laura Moreno, Fernandez, Lucia, Frare, Pablo S., Chaio, Sebastian R., Pachioli, Valeria, Timpano, Stella M., Sanchez, Marisa del Lujan, de Paz Sierra, Mariana, Stanek, Vanina, Belloso, Waldo, Cilenti, Flavia L., Valentini, Ricardo N., Stryjewski, Martin E., Locatelli, Nicolas, Soler Riera, Maria C., Salgado, Clara, Baeck, Ines M., Di Castelnuovo, Valentina, Zarza, Stella M., Hudson, Fleur, Parmar, Mahesh K.B., Goodman, Anna L., Dphil, Badrock, Jonathan, Gregory, Adam, Goodall, Katharine, Harris, Nicola, Wyncoll, James, Bhagani, S., Rodger, A., Luntiel, A., Patterson, C., Morales, J., Witele, E., Preston, A.-M., Nandani, A., Price, D.A., Hanrath, Aiden, Nell, Jeremy, Patel, Bijal, Hays, Carole, Jones, Geraldine, Davidson, Jade, Bawa, T., Mathews, M., Mazzella, A., Bisnauthsing, K., Aguilar-Jimenez, L., Borchini, F., Hammett, S., Touloumi, Giota, Pantazis, Nikos, Gioukari, Vicky, Souliou, Tania, Antoniadou, A., Protopapas, K., Kavatha, D., Grigoropoulou, S., Oikonomopoulo, C., Moschopoulos, C., Koulouris, N.G., Tzimopoulos, K., Koromilias, A., Argyraki, K., Lourida, P., Bakakos, P., Kalomenidis, I., Vlachakos, V., Barmparessou, Z., Balis, E., Zakynthinos, S., Sigala, I., Gianniou, N., Dima, E., Magkouta, S., Synolaki, E., Konstanta, S., Vlachou, M., Stathopoulou, P., Panagopoulos, P., Petrakis, V., Papazoglou, D., Tompaidou, E., Isaakidou, E., Poulakou, G., Rapti, V., Leontis, K., Nitsotolis, T., Athanasiou, K., Syrigos, K., Kyriakoulis, K., Trontzas, I., Arfara-Melanini, M., Kolonis, V., Kityo, Cissy, Mugerwa, Henry, Kiweewa, Francis, Kimuli, Ivan, Lukaakome, Joseph, Nsereko, Christoher, Lubega, Gloria, Kibirige, Moses, Nakahima, William, Wangi, Deus, Aguti, Evelyne, Generous, Lilian, Massa, Rosemary, Nalaki, Margaret, Magala, Felix, Nabaggala, Phiona Kaweesi, Kidega, Robert, Faith, Oryem Daizy, Florence, Apio, Emmanuel, Ocung, Beacham, Mugoonyi Paul, Geoffrey, Amone, Nakiboneka, Dridah, Apiyo, Paska, Kirenga, Bruce, Atukunda, Angella, Muttamba, Winters, Remmy, Kyeyume, Segawa, Ivan, Pheona, Nsubuga, Kigere, David, Mbabazi, Queen Lailah, Boersalino, Ledra, Nyakoolo, Grace, Fred, Aniongo, Alupo, Alice, Ebong, Doryn, Monday, Edson, Nalubwama, Ritah Norah, Kainja, Milton, Ambrose, Munu, Kwehayo, Vanon, Nalubega, Mary Grace, Ongoli, Augustine, Obbo, Stephen, Sebudde, Nicholus, Alaba, Jeniffer, Magombe, Geoffrey, Tino, Harriet, Obonya, Emmanuel, Lutaakome, Joseph, Kitonsa, Jonathan, Onyango, Martin, Naboth, Tukamwesiga, Naluyinda, Hadijah, Nanyunja, Regina, Irene, Muttiibwa, Jane, Biira, Wimfred, Kyobejja, Leonard, Ssemazzi, Deus, Tkiinomuhisha, Babra, Namasaba, Taire, Paul, Nabankema, Evelyn, Ogavu, Joseph, Mugerwa, Oscar, Okoth, Ivan, Mwebaze, Raymond, Mugabi, Timothy, Makhoba, Anthony, Arikiriza, Phiona, Theresa, Nabuuma, Nakayima, Hope, Frank, Kisuule, Ramgi, Patrícia, Pereira, Kássia, Osinusi, Anu, Cao, Huyen, Klekotka, Paul, Price, Karen, Nirula, Ajay, Osei, Suzette, Tipple, Craig, Wills, Angela, Peppercorn, Amanda, Watson, Helen, Gupta, Rajesh, Alexander, Elizabeth, Mogalian, Erik, Lin, Leo, Ding, Xiao, Margolis, David, Yan, Li, Girardet, Jean-Luc, Ma, Ji, Hong, Zhi, Zhu, Quing, Seegobin, Seth, Gibbs, Michael, Latchman, Mickel, Hasior, Katarzyna, Bouquet, Jerome, Wei, Jianxin, Streicher, Katie, Schmelzer, Albert, Brooks, Dennis, Butcher, Jonny, Tonev, Dimitar, Arbetter, Douglas, Damstetter, Philippe, Legenne, Philippe, Stumpp, Michael, Goncalves, Susana, Ramanathan, Krishnan, Chandra, Richa, Baseler, Beth, Teitelbaum, Marc, Schechner, Adam, Holley, H. Preston, Jankelevich, Shirley, Becker, Nancy, Dolney, Suzanne, Hissey, Debbie, Simpson, Shelly, Kim, Mi Ha, Beeler, Joy, Harmon, Liam, Asomah, Mabel, Jato, Yvonne, Stottlemyer, April, Tang, Olivia, Vanderpuye, Sharon, Yeon, Lindsey, Buehn, Molly, Eccard-Koons, Vanessa, Frary, Sadie, MacDonald, Leah, Cash, Jennifer, Hoopengardner, Lisa, Linton, Jessica, Schaffhauser, Marylu, Nelson, Michaela, Spinelli-Nadzam, Mary, Proffitt, Calvin, Lee, Christopher, Engel, Theresa, Fontaine, Laura, Osborne, C.K., Hohn, Matt, Galcik, Michael, Thompson, DeeDee, Kopka, Stacey, Shelley, Denise M., Mendez, Gregg, Brown, Shawn, Albert, Sara, Balde, Abby, Baracz, Michelle, Bielica, Mona, Billouin-Frazier, Shere, Choudary, Jay, Dixon, Mary, Eyler, Carolyn, Frye, Leanne, Gertz, Jensen, Giebeig, Lisa, Gulati, Neelam, Hankinson, Liz, Hogarty, Debi, Huber, Lynda, Krauss, Gary, Lake, Eileen, Manandhar, Meryan, Rudzinski, Erin, Sandrus, Jen, Suders, Connie, Natarajan, Ven, Rupert, Adam W., Baseler, Michael, Lynam, Danielle, Imamichi, Tom, Laverdure, Sylvain, McCormack, Ashley, Paudel, Sharada, Cook, Kyndal, Haupt, Kendra, Khan, Ayub, Hazen, Allison, Badralmaa, Yunden, Smith, Kenneth, Patel, Bhakti, Kubernac, Amanda, Kubernac, Robert, Hoover, Marie L., Solomon, Courtney, Rashid, Marium, Murphy, Joseph, Brown, Craig, DuChateau, Nadine, Ellis, Sadie, Flosi, Adam, Fox, Lisa, Johnson, Les, Nelson, Rich, Stojanovic, Jelena, Treagus, Amy, Wenner, Christine, Williams, Richard, Jensen, Tomas O, Murray, Thomas A, Grandits, Greg A, Jain, Mamta K, Shaw-Saliba, Kathryn, Matthay, Michael A, Baker, Jason V, Dewar, Robin L, Goodman, Anna L, Hatlen, Timothy J, Highbarger, Helene C, Lallemand, Perrine, Leshnower, Bradley G, Looney, David, Moschopoulos, Charalampos D, Murray, Daniel D, Mylonakis, Eleftherios, Rehman, M Tauseef, Rupert, Adam, Stevens, Randy, Turville, Stuart, Wick, Katherine, Lundgren, Jens, and Ko, Emily R

Published
2024
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17. A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses

Author

Gebrechorkos, Solomon, Leyland, Julian, Slater, Louise, Wortmann, Michel, Ashworth, Philip J., Bennett, Georgina L., Boothroyd, Richard, Cloke, Hannah, Delorme, Pauline, Griffith, Helen, Hardy, Richard, Hawker, Laurence, McLelland, Stuart, Neal, Jeffrey, Nicholas, Andrew, Tatem, Andrew J., Vahidi, Ellie, Parsons, Daniel R., and Darby, Stephen E.

Published
2023
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20. Polymicrobial Purulent Pericarditis From a Pancreatico-Pericardial Fistula

Author

Christopher Richardson, DO, Mark Cromer, MD, Luis Tatem, MD, Raymond Wade, MD, and Derek Russell, MD

Subjects
computed tomography, imaging, pancreatic pseudocyst, pancreatitis, pericardial effusion, pericarditis, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

A 54-year-old male with chronic pancreatitis presented with dyspnea. Computed tomography scans demonstrated a subdiaphragmatic fluid collection with pericardial fistulization. Pericardial fluid cultures were polymicrobial in nature. Purulent pericarditis is rare but carries a high mortality rate. We present the first documented case of pancreatico-pericardial fistulization causing purulent pericarditis.

Published
2024
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21. High-resolution population estimation using household survey data and building footprints.

Author

Boo, Gianluca, Darin, Edith, Leasure, Douglas R, Dooley, Claire A, Chamberlain, Heather R, Lázár, Attila N, Tschirhart, Kevin, Sinai, Cyrus, Hoff, Nicole A, Fuller, Trevon, Musene, Kamy, Batumbo, Arly, Rimoin, Anne W, and Tatem, Andrew J

Subjects
Bayes Theorem, Uncertainty, Censuses, Behavioral and Social Science, Basic Behavioral and Social Science
Abstract

The national census is an essential data source to support decision-making in many areas of public interest. However, this data may become outdated during the intercensal period, which can stretch up to several decades. In this study, we develop a Bayesian hierarchical model leveraging recent household surveys and building footprints to produce up-to-date population estimates. We estimate population totals and age and sex breakdowns with associated uncertainty measures within grid cells of approximately 100 m in five provinces of the Democratic Republic of the Congo, a country where the last census was completed in 1984. The model exhibits a very good fit, with an R2 value of 0.79 for out-of-sample predictions of population totals at the microcensus-cluster level and 1.00 for age and sex proportions at the province level. This work confirms the benefits of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses.

Published
2022

22. High-resolution population estimation using household survey data and building footprints

Author

Boo, Gianluca, Darin, Edith, Leasure, Douglas R, Dooley, Claire A, Chamberlain, Heather R, Lázár, Attila N, Tschirhart, Kevin, Sinai, Cyrus, Hoff, Nicole A, Fuller, Trevon, Musene, Kamy, Batumbo, Arly, Rimoin, Anne W, and Tatem, Andrew J

Subjects
Statistics - Applications, 62, I.6.5
Abstract

The national census is an essential data source to support decision-making in many areas of public interest. However, this data may become outdated during the intercensal period, which can stretch up to several decades. We developed a Bayesian hierarchical model leveraging recent household surveys with probabilistic sampling designs and building footprints to produce up-to-date population estimates. We estimated population totals and age and sex breakdowns with associated uncertainty measures within grid cells of approximately 100m in five provinces of the Democratic Republic of the Congo, a country where the last census was completed in 1984. The model exhibited a very good fit, with an R^2 value of 0.79 for out-of-sample predictions of population totals at the microcensus-cluster level and 1.00 for age and sex proportions at the province level. The results confirm the benefits of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses., Comment: 27 pages, 6 figures

Published
2021
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23. Imported and indigenous Plasmodium Vivax and Plasmodium Falciparum malaria in the Hubei Province of China, 2005–2019

Author

Dongni Wu, Hong Zhu, Lun Wan, Juan Zhang, Wen Lin, Lingcong Sun, Huaxun Zhang, Si Liu, Eimear Cleary, Andrew J. Tatem, Jing Xia, and Shengjie Lai

Subjects
China, Elimination, Epidemiology, Imported cases, Malaria, Arctic medicine. Tropical medicine, RC955-962, Infectious and parasitic diseases, RC109-216
Abstract

Abstract Background The Hubei Province in China reported its last indigenous malaria case in September 2012, but imported malaria cases, particularly those related to Plasmodium vivax and Plasmodium falciparum, threaten Hubei’s malaria-free status. This study investigated the epidemiological changes in P. vivax and P. falciparum malaria in this province to provide scientific evidence for preventing malaria resurgence. Methods The prevalence, demographic characteristics, seasonal features, and geographical distribution of malaria were assessed using surveillance data and were compared across three stages: control stage (2005–2009) and elimination stages I (2010–2014) and II (2015–2019). Results In 2005–2019, 8483 malaria cases were reported, including 5599 indigenous P. vivax cases, 275 imported P. vivax cases, 866 imported P. falciparum cases, and 1743 other cases. Imported P. falciparum cases accounted for 0.07% of all cases reported in 2005, but increased to 78.81% in 2019. Most imported P. vivax and P. falciparum malaria occurred among males, aged 21–60 years, during elimination stages I and II. The number of regions affected by imported P. falciparum and P. vivax increased markedly in Hubei from the control stage to elimination stage II. Overall, 1125 imported P. vivax and P. falciparum cases were detected from 47 other nations. Eight imported cases were detected from other provinces in China. From the control stage to elimination stage II, the number of cases of malaria imported from African countries increased, and that of cases imported from Southeast Asian countries decreased. Conclusions Although Hubei has achieved malaria elimination, it faces challenges in maintaining this status. Hence, imported malaria surveillance need to be strengthened to reduce the risk of malaria re-introduction.

Published
2023
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24. A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses

Author

Solomon Gebrechorkos, Julian Leyland, Louise Slater, Michel Wortmann, Philip J. Ashworth, Georgina L. Bennett, Richard Boothroyd, Hannah Cloke, Pauline Delorme, Helen Griffith, Richard Hardy, Laurence Hawker, Stuart McLelland, Jeffrey Neal, Andrew Nicholas, Andrew J. Tatem, Ellie Vahidi, Daniel R. Parsons, and Stephen E. Darby

Subjects
Science
Abstract

Abstract A large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events. The globally downscaled data are available at the Centre for Environmental Data Analysis ( https://doi.org/10.5285/c107618f1db34801bb88a1e927b82317 ) for the historical (1981–2014) and future (2015–2100) periods at 0.25° resolution and at daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4-OS and SSP5-8.5). This new climate dataset will be useful for assessing future changes and variability in climate and for driving high-resolution impact assessment models.

Published
2023
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26. Effects of public-health measures for zeroing out different SARS-CoV-2 variants

Author

Yong Ge, Xilin Wu, Wenbin Zhang, Xiaoli Wang, Die Zhang, Jianghao Wang, Haiyan Liu, Zhoupeng Ren, Nick W. Ruktanonchai, Corrine W. Ruktanonchai, Eimear Cleary, Yongcheng Yao, Amy Wesolowski, Derek A. T. Cummings, Zhongjie Li, Andrew J. Tatem, and Shengjie Lai

Subjects
Science
Abstract

Abstract Targeted public health interventions for an emerging epidemic are essential for preventing pandemics. During 2020-2022, China invested significant efforts in strict zero-COVID measures to contain outbreaks of varying scales caused by different SARS-CoV-2 variants. Based on a multi-year empirical dataset containing 131 outbreaks observed in China from April 2020 to May 2022 and simulated scenarios, we ranked the relative intervention effectiveness by their reduction in instantaneous reproduction number. We found that, overall, social distancing measures (38% reduction, 95% prediction interval 31-45%), face masks (30%, 17-42%) and close contact tracing (28%, 24-31%) were most effective. Contact tracing was crucial in containing outbreaks during the initial phases, while social distancing measures became increasingly prominent as the spread persisted. In addition, infections with higher transmissibility and a shorter latent period posed more challenges for these measures. Our findings provide quantitative evidence on the effects of public-health measures for zeroing out emerging contagions in different contexts.

Published
2023
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27. High-resolution gridded population datasets for Latin America and the Caribbean using official statistics

Author

Tom McKeen, Maksym Bondarenko, David Kerr, Thomas Esch, Mattia Marconcini, Daniela Palacios-Lopez, Julian Zeidler, R. Catalina Valle, Sabrina Juran, Andrew J. Tatem, and Alessandro Sorichetta

Subjects
Science
Abstract

Abstract “Leaving no one behind” is the fundamental objective of the 2030 Agenda for Sustainable Development. Latin America and the Caribbean is marked by social inequalities, whilst its total population is projected to increase to almost 760 million by 2050. In this context, contemporary and spatially detailed datasets that accurately capture the distribution of residential population are critical to appropriately inform and support environmental, health, and developmental applications at subnational levels. Existing datasets are under-utilised by governments due to the non-alignment with their own statistics. Therefore, official statistics at the finest level of administrative units available have been implemented to construct an open-access repository of high-resolution gridded population datasets for 40 countries in Latin American and the Caribbean. These datasets are detailed here, alongside the ‘top-down’ approach and methods to generate and validate them. Population distribution datasets for each country were created at a resolution of 3 arc-seconds (approximately 100 m at the equator), and are all available from the WorldPop Data Repository.

Published
2023
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28. The COVID-19 pandemic as experienced by the individual

Author

Garland, Patrick, Babbitt, Dave, Bondarenko, Maksym, Sorichetta, Alessandro, Tatem, Andrew J., and Johnson, Oliver

Subjects
Physics - Physics and Society, Quantitative Biology - Populations and Evolution, Statistics - Applications
Abstract

The ongoing COVID-19 pandemic has progressed with varying degrees of intensity in individual countries, suggesting it is important to analyse factors that vary between them. We study measures of `population-weighted density', which capture density as perceived by a randomly chosen individual. These measures of population density can significantly explain variation in the initial rate of spread of COVID-19 between countries within Europe. However, such measures do not explain differences on a global scale, particularly when considering countries in East Asia, or looking later into the epidemics. Therefore, to control for country-level differences in response to COVID-19 we consider the cross-cultural measure of individualism proposed by Hofstede. This score can significantly explain variation in the size of epidemics across Europe, North America, and East Asia. Using both our measure of population-weighted density and the Hofstede score we can significantly explain half the variation in the current size of epidemics across Europe and North America. By controlling for country-level responses to the virus and population density, our analysis of the global incidence of COVID-19 can help focus attention on epidemic control measures that are effective for individual countries.

Published
2020

29. Untangling introductions and persistence in COVID-19 resurgence in Europe

Author

Lemey, Philippe, Ruktanonchai, Nick, Hong, Samuel L, Colizza, Vittoria, Poletto, Chiara, Van den Broeck, Frederik, Gill, Mandev S, Ji, Xiang, Levasseur, Anthony, Oude Munnink, Bas B, Koopmans, Marion, Sadilek, Adam, Lai, Shengjie, Tatem, Andrew J, Baele, Guy, Suchard, Marc A, and Dellicour, Simon

Subjects
Infectious Diseases, Prevention, Infection, Good Health and Well Being, COVID-19, Europe, Genome, Viral, Humans, Incidence, Locomotion, Phylogeny, Phylogeography, SARS-CoV-2, Time Factors, Travel, General Science & Technology
Abstract

After the first wave of SARS-CoV-2 infections in spring 2020, Europe experienced a resurgence of the virus starting in late summer 2020 that was deadlier and more difficult to contain1. Relaxed intervention measures and summer travel have been implicated as drivers of the second wave2. Here we build a phylogeographical model to evaluate how newly introduced lineages, as opposed to the rekindling of persistent lineages, contributed to the resurgence of COVID-19 in Europe. We inform this model using genomic, mobility and epidemiological data from 10 European countries and estimate that in many countries more than half of the lineages circulating in late summer resulted from new introductions since 15 June 2020. The success in onward transmission of newly introduced lineages was negatively associated with the local incidence of COVID-19 during this period. The pervasive spread of variants in summer 2020 highlights the threat of viral dissemination when restrictions are lifted, and this needs to be carefully considered in strategies to control the current spread of variants that are more transmissible and/or evade immunity. Our findings indicate that more effective and coordinated measures are required to contain the spread through cross-border travel even as vaccination is reducing disease burden.

Published
2021

32. A subnational reproductive, maternal, newborn, child, and adolescent health and development atlas of India

Author

Carla Pezzulo, Natalia Tejedor-Garavito, Ho Man Theophilus Chan, Ilda Dreoni, David Kerr, Samik Ghosh, Amy Bonnie, Maksym Bondarenko, Mihretab Salasibew, and Andrew J. Tatem

Subjects
Science
Abstract

Abstract Understanding the fine scale and subnational spatial distribution of reproductive, maternal, newborn, child, and adolescent health and development indicators is crucial for targeting and increasing the efficiency of resources for public health and development planning. National governments are committed to improve the lives of their people, lift the population out of poverty and to achieve the Sustainable Development Goals. We created an open access collection of high resolution gridded and district level health and development datasets of India using mainly the 2015–16 National Family Health Survey (NFHS-4) data, and provide estimates at higher granularity than what is available in NFHS-4, to support policies with spatially detailed data. Bayesian methods for the construction of 5 km × 5 km high resolution maps were applied for a set of indicators where the data allowed (36 datasets), while for some other indicators, only district level data were produced. All data were summarised using the India district administrative boundaries. In total, 138 high resolution and district level datasets for 28 indicators were produced and made openly available.

Published
2023
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33. Mapping urban physical distancing constraints, sub-Saharan Africa: a case study from Kenya/Cartographie des contraintes urbaines en matiere de distanciation physique en Afrique subsaharienne: etude de cas au Kenya/Cartografia de las limitaciones del distanciamiento fisico en el Africa subsahariana: un estudio de caso de Kenia

Author

Chamberlain, Heather R., Macharia, Peter M., and Tatem, Andrew J.

Subjects
Epidemics -- Case studies -- United Kingdom -- Sub-Saharan Africa -- Kenya, Disease transmission -- Case studies, Geospatial data -- Case studies, Coronaviruses -- Case studies, Health
Abstract

With the onset of the coronavirus disease 2019 (COVID-19) pandemic, public health measures such as physical distancing were recommended to reduce transmission of the virus causing the disease. However, the same approach in all areas, regardless of context, may lead to measures being of limited effectiveness and having unforeseen negative consequences, such as loss of livelihoods and food insecurity. A prerequisite to planning and implementing effective, context-appropriate measures to slow community transmission is an understanding of any constraints, such as the locations where physical distancing would not be possible. Focusing on sub-Saharan Africa, we outline and discuss challenges that are faced by residents of urban informal settlements in the ongoing COVID-19 pandemic. We describe how new geospatial data sets can be integrated to provide more detailed information about local constraints on physical distancing and can inform planning of alternative ways to reduce transmission of COVID-19 between people. We include a case study for Nairobi County, Kenya, with mapped outputs which illustrate the intra-urban variation in the feasibility of physical distancing and the expected difficulty for residents of many informal settlement areas. Our examples demonstrate the potential of new geospatial data sets to provide insights and support to policy-making for public health measures, including COVID-19. [phrase omitted] Avec l'apparition de la pandemie de maladie a coronavirus 2019 (COVID-19), des mesures de sante publique telles que la distanciation physique ont ete mises en place afin de limiter la transmission du virus a l'origine de la maladie. Neanmoins, adopter la meme approche dans toutes les regions sans tenir compte du contexte pourrait reduire l'efficacite de ces mesures et avoir des consequences negatives imprevues, comme la perte des moyens de subsistance et l'insecurite alimentaire. Avant de planifier et de deployer des mesures utiles et adaptees a la situation en vue de ralentir la transmission au sein des communautes, il est imperatif d'identifier les contraintes liees notamment aux lieux oU la distanciation physique est impossible a respecter. Le present document se concentre sur l'Afrique subsaharienne. Nous y avons presente et evoque les defis auxquels sont confrontes les habitants des implantations urbaines sauvages au cours de l'actuelle pandemie de COVID-19. Nous decrivons comment integrer les nouveaux ensembles de donnees geospatiales pour obtenir des informations plus detaillees sur les contraintes locales liees a la distanciation physique et trouver des solutions alternatives permettant de limiter la transmission de la COVID-19 d'une personne a l'autre. Nous citons une etude de cas realisee dans le comte de Nairobi, au Kenya, dont les resultats cartographies illustrent les variations intra-urbaines qui determinent la faisabilite de la distanciation physique et les difficultes que les habitants de nombreuses implantations sauvages sont susceptibles de rencontrer. Nos exemples revelent le potentiel des nouveaux ensembles de donnees geospatiales dans l'analyse et l'elaboration des politiques et mesures de sante publique, y compris pour la COVID-19. [phrase omitted] Con el inicio de la pandemia de la enfermedad por coronavirus de 2019 (COVID-19), se recomendaron medidas de salud publica como el distanciamiento fisico para reducir la transmision del virus causante de la enfermedad. Sin embargo, el mismo enfoque en todas las areas, sin tener en cuenta el contexto, puede llevar a que las medidas sean de eficacia limitada y tengan consecuencias negativas imprevistas, como la perdida de medios de vida y la inseguridad alimentaria. Un requisito previo para planificar y aplicar medidas eficaces y adecuadas al contexto para ralentizar la transmision en la comunidad es conocer las limitaciones, como los lugares en los que no seria posible el distanciamiento fisico. En este documento, centrado en el Africa subsahariana, se describen y discuten los desafios a los que se enfrentan los residentes de los asentamientos urbanos informales en la actual pandemia de la COVID-19. Se describe como los nuevos conjuntos de datos geoespaciales pueden integrarse para proporcionar informacion mas detallada sobre las limitaciones locales al distanciamiento fisico y pueden informar la planificacion de vias alternativas para reducir la transmision de la COVID-19 entre las personas. Se incluye un estudio de caso del condado de Nairobi, Kenia, con resultados cartograficos que ilustran la variacion intraurbana en la viabilidad del distanciamiento fisico y la dificultad prevista para los residentes de muchas areas de asentamientos informales. Los ejemplos que aqui se presentan demuestran el potencial de los nuevos conjuntos de datos geoespaciales para proporcionar informacion y apoyo a la elaboracion de politicas sobre medidas de salud publica, entre ellas las relacionadas con la COVID-19., Introduction The need for context-appropriate public health measures to slow or interrupt community transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 2019 (COVID-19) pandemic was [...]

Published
2022
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34. Distinct rates and patterns of spread of the major HIV-1 subtypes in Central and East Africa.

Author

Faria, Nuno R, Vidal, Nicole, Lourenco, José, Raghwani, Jayna, Sigaloff, Kim CE, Tatem, Andy J, van de Vijver, David AM, Pineda-Peña, Andrea-Clemencia, Rose, Rebecca, Wallis, Carole L, Ahuka-Mundeke, Steve, Muyembe-Tamfum, Jean-Jacques, Muwonga, Jérémie, Suchard, Marc A, Rinke de Wit, Tobias F, Hamers, Raph L, Ndembi, Nicaise, Baele, Guy, Peeters, Martine, Pybus, Oliver G, Lemey, Philippe, and Dellicour, Simon

Subjects
Humans, HIV-1, HIV Infections, Africa, Central, Africa, Eastern, Africa, Central, Eastern, Virology, Microbiology, Immunology, Medical Microbiology
Abstract

Since the ignition of the HIV-1 group M pandemic in the beginning of the 20th century, group M lineages have spread heterogeneously throughout the world. Subtype C spread rapidly through sub-Saharan Africa and is currently the dominant HIV lineage worldwide. Yet the epidemiological and evolutionary circumstances that contributed to its epidemiological expansion remain poorly understood. Here, we analyse 346 novel pol sequences from the DRC to compare the evolutionary dynamics of the main HIV-1 lineages, subtypes A1, C and D. Our results place the origins of subtype C in the 1950s in Mbuji-Mayi, the mining city of southern DRC, while subtypes A1 and D emerged in the capital city of Kinshasa, and subtypes H and J in the less accessible port city of Matadi. Following a 15-year period of local transmission in southern DRC, we find that subtype C spread at least three-fold faster than other subtypes circulating in Central and East Africa. In conclusion, our results shed light on the origins of HIV-1 main lineages and suggest that socio-historical rather than evolutionary factors may have determined the epidemiological fate of subtype C in sub-Saharan Africa.

Published
2019

35. Assessing spread risk of COVID-19 in early 2020

Author

Shengjie Lai, Isaac I. Bogoch, Nick W. Ruktanonchai, Alexander Watts, Xin Lu, Weizhong Yang, Hongjie Yu, Kamran Khan, and Andrew J. Tatem

Subjects
COVID-19, Human mobility, Transmission, Mobile phone, Air travel, Electronic computers. Computer science, QA75.5-76.95
Abstract

A novel coronavirus emerged in late 2019, named as the coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO). This study was originally conducted in January 2020 to estimate the potential risk and geographic range of COVID-19 spread at the early stage of the transmission. A series of connectivity and risk analyses based on domestic and international travel networks were conducted using historical aggregated mobile phone data and air passenger itinerary data. We found that the cordon sanitaire of the primary city was likely to have occurred during the latter stages of peak population numbers leaving the city, with travellers departing into neighbouring cities and other megacities in China. We estimated that there were 59,912 international air passengers, of which 834 (95% uncertainty interval: 478–1,349) had COVID-19 infection, with a strong correlation seen between the predicted risks of importation and the number of imported cases found. Given the limited understanding of emerging infectious diseases in the very early stages of outbreaks, our approaches and findings in assessing travel patterns and risk of transmission can help guide public health preparedness and intervention design for new COVID-19 waves caused by variants of concern and future pandemics to effectively limit transmission beyond its initial extent.

Published
2022
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40. Geographic inequalities in health intervention coverage – mapping the composite coverage index in Peru using geospatial modelling

Author

Leonardo Z. Ferreira, C. Edson Utazi, Luis Huicho, Kristine Nilsen, Fernando P. Hartwig, Andrew J. Tatem, and Aluisio J. D. Barros

Subjects
Geospatial modelling, Child health, Woman’s health, Composite coverage index, Peru, Public aspects of medicine, RA1-1270
Abstract

Abstract Background The composite coverage index (CCI) provides an integrated perspective towards universal health coverage in the context of reproductive, maternal, newborn and child health. Given the sample design of most household surveys does not provide coverage estimates below the first administrative level, approaches for achieving more granular estimates are needed. We used a model-based geostatistical approach to estimate the CCI at multiple resolutions in Peru. Methods We generated estimates for the eight indicators on which the CCI is based for the departments, provinces, and areas of 5 × 5 km of Peru using data from two national household surveys carried out in 2018 and 2019 plus geospatial covariates. Bayesian geostatistical models were fit using the INLA-SPDE approach. We assessed model fit using cross-validation at the survey cluster level and by comparing modelled and direct survey estimates at the department-level. Results CCI coverage in the provinces along the coast was consistently higher than in the remainder of the country. Jungle areas in the north and east presented the lowest coverage levels and the largest gaps between and within provinces. The greatest inequalities were found, unsurprisingly, in the largest provinces where populations are scattered in jungle territory and are difficult to reach. Conclusions Our study highlighted provinces with high levels of inequality in CCI coverage indicating areas, mostly low-populated jungle areas, where more attention is needed. We also uncovered other areas, such as the border with Bolivia, where coverage is lower than the coastal provinces and should receive increased efforts. More generally, our results make the case for high-resolution estimates to unveil geographic inequities otherwise hidden by the usual levels of survey representativeness.

Published
2022
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41. Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa

Author

Fleur Hierink, Gianluca Boo, Peter M. Macharia, Paul O. Ouma, Pablo Timoner, Marc Levy, Kevin Tschirhart, Stefan Leyk, Nicholas Oliphant, Andrew J. Tatem, and Nicolas Ray

Subjects
Medicine
Abstract

Hierink et al. examine the impact of commonly used gridded population datasets on measures of geographic accessibility to healthcare facilities in sub-Saharan Africa. The authors report substantial differences in accessibility depending on the dataset used, particularly in sparsely populated areas.

Published
2022
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42. Geospatial Analyses of Recent Household Surveys to Assess Changes in the Distribution of Zero-Dose Children and Their Associated Factors before and during the COVID-19 Pandemic in Nigeria

Author

Justice Moses K. Aheto, Iyanuloluwa Deborah Olowe, Ho Man Theophilus Chan, Adachi Ekeh, Boubacar Dieng, Biyi Fafunmi, Hamidreza Setayesh, Brian Atuhaire, Jessica Crawford, Andrew J. Tatem, and Chigozie Edson Utazi

Subjects
MCV1 coverage, DTP1 coverage, composite coverage, zero-dose prevalence, Demographic and Health Surveys, Multiple Indicator Cluster Survey, Medicine
Abstract

The persistence of geographic inequities in vaccination coverage often evidences the presence of zero-dose and missed communities and their vulnerabilities to vaccine-preventable diseases. These inequities were exacerbated in many places during the coronavirus disease 2019 (COVID-19) pandemic, due to severe disruptions to vaccination services. Understanding changes in zero-dose prevalence and its associated risk factors in the context of the COVID-19 pandemic is, therefore, critical to designing effective strategies to reach vulnerable populations. Using data from nationally representative household surveys conducted before the COVID-19 pandemic, in 2018, and during the pandemic, in 2021, in Nigeria, we fitted Bayesian geostatistical models to map the distribution of three vaccination coverage indicators: receipt of the first dose of diphtheria-tetanus-pertussis-containing vaccine (DTP1), the first dose of measles-containing vaccine (MCV1), and any of the four basic vaccines (bacilli Calmette-Guerin (BCG), oral polio vaccine (OPV0), DTP1, and MCV1), and the corresponding zero-dose estimates independently at a 1 × 1 km resolution and the district level during both time periods. We also explored changes in the factors associated with non-vaccination at the national and regional levels using multilevel logistic regression models. Our results revealed no increases in zero-dose prevalence due to the pandemic at the national level, although considerable increases were observed in a few districts. We found substantial subnational heterogeneities in vaccination coverage and zero-dose prevalence both before and during the pandemic, showing broadly similar patterns in both time periods. Areas with relatively higher zero-dose prevalence occurred mostly in the north and a few places in the south in both time periods. We also found consistent areas of low coverage and high zero-dose prevalence using all three zero-dose indicators, revealing the areas in greatest need. At the national level, risk factors related to socioeconomic/demographic status (e.g., maternal education), maternal access to and utilization of health services, and remoteness were strongly associated with the odds of being zero dose in both time periods, while those related to communication were mostly relevant before the pandemic. These associations were also supported at the regional level, but we additionally identified risk factors specific to zero-dose children in each region; for example, communication and cross-border migration in the northwest. Our findings can help guide tailored strategies to reduce zero-dose prevalence and boost coverage levels in Nigeria.

Published
2023
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43. Upward-Pointing Cosmic-Ray-like Events Observed with ANITA

Author

Romero-Wolf, Andres, Gorham, P. W., Nam, J., Hoover, S., Allison, P., Banerjee, O., Batten, L., Beatty, J. J., Belov, K., Besson, D. Z., Binns, W. R., Bugaev, V., Cao, P., Chen, C., Chen, P., Clem, J. M., Connolly, A., Dailey, B., Deaconu, C., Cremonesi, L., Dowkontt, P. F., DuVernois, M. A., Field, R. C., Fox, B. D., Goldstein, D., Gordon, J., Hast, C., Hebert, C. L., Hill, B., Hughes, K., Hupe, R., Israel, M. H., Javaid, A., Kowalski, J., Lam, J., Ludwig, A., Learned, J. G., Liewer, K. M., Liu, T. C., Link, J. T., Lusczek, E., Matsuno, S., Mercurio, B. C., Miki, C., Miocinovic, P., Mottram, M., Mulrey, K., Naudet, C. J., Ng, J., Nichol, R. J., Novikov, A., Palladino, K., Prohira, S., Rauch, B. F., Reil, K., Roberts, J., Rosen, M., Rotter, B., Russell, J., Ruckman, L., Saltzberg, D., Seckel, D., Stafford, S., Stockham, J., Stockham, M., Strutt, B., Tatem, K., Varner, G. S., Vieregg, A. G., Walz, D., Wissel, S. A., Wu, F., Alvarez-Muñiz, J., Carvalho Jr., W., Schoorlemmer, H., and Zas, E.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

These proceedings address a recent publication by the ANITA collaboration of four upward- pointing cosmic-ray-like events observed in the first flight of ANITA. Three of these events were consistent with stratospheric cosmic-ray air showers where the axis of propagation does not inter- sect the surface of the Earth. The fourth event was consistent with a primary particle that emerges from the surface of the ice suggesting a possible {\tau}-lepton decay as the origin of this event. These proceedings follow-up on the modeling and testing of the hypothesis that this event was of {\tau} neutrino origin., Comment: 8 pages, 3 figures, presented at the International Cosmic Ray Conference 2017, Busan, South Korea

Published
2018

44. Mapping road network communities for guiding disease surveillance and control strategies

Author

Strano, Emanuele, Viana, Matheus P., Sorichetta, Alessandro, and Tatem, Andrew J.

Subjects
Physics - Physics and Society
Abstract

Human mobility is increasing in its volume, speed and reach, leading to the movement and introduction of pathogens through infected travelers. An understanding of how areas are connected, the strength of these connections and how this translates into disease spread is valuable for planning surveillance and designing control and elimination strategies. While analyses have been undertaken to identify and map connectivity in global air, shipping and migration networks, such analyses have yet to be undertaken on the road networks that carry the vast majority of travellers in low and middle income settings. Here we present methods for identifying road connectivity communities, as well as mapping bridge areas between communities and key linkage routes. We apply these to Africa, and show how many highly-connected communities straddle national borders and when integrating malaria prevalence and population data as an example, the communities change, highlighting regions most strongly connected to areas of high burden. The approaches and results presented provide a flexible tool for supporting the design of disease surveillance and control strategies through mapping areas of high connectivity that form coherent units of intervention and key link routes between communities for targeting surveillance., Comment: 11 pages, 5 figures, research paper

Published
2018

45. Antarctic Surface Reflectivity Calculations and Measurements from the ANITA-4 and HiCal-2 Experiments

Author

Prohira, S., Novikov, A., Dasgupta, P., Jain, P., Nande, S., Allison, P., Banerjee, O., Batten, L., Beatty, J. J., Belov, K., Besson, D. Z., Binns, W. R., Bugaev, V., Cao, P., Chen, C., Chen, P., Clem, J. M., Connolly, A., Cremonesi, L., Dailey, B., Deaconu, C., Dowkontt, P. F., Fox, B. D., Gordon, J., Gorham, P. W., Hast, C., Hill, B., Hupe, R., Israel, M. H., Lam, J., Liu, T. C., Ludwig, A., Matsuno, S., Miki, C., Mottram, M., Mulrey, K., Nam, J., Nichol, R. J., Oberla, E., Ratzlaff, K., Rauch, B. F., Romero-Wolf, A., Rotter, B., Russell, J., Saltzberg, D., Seckel, D., Schoorlemmer, H., Stafford, S., Stockham, J., Stockham, M., Strutt, B., Tatem, K., Varner, G. S., Vieregg, A. G., Wissel, S. A., Wu, F., and Young, R.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The balloon-borne HiCal radio-frequency (RF) transmitter, in concert with the ANITA radio-frequency receiver array, is designed to measure the Antarctic surface reflectivity in the RF wavelength regime. The amplitude of surface-reflected transmissions from HiCal, registered as triggered events by ANITA, can be compared with the direct transmissions preceding them by O(10) microseconds, to infer the surface power reflection coefficient $\cal{R}$. The first HiCal mission (HiCal-1, Jan. 2015) yielded a sample of 100 such pairs, resulting in estimates of $\cal{R}$ at highly-glancing angles (i.e., zenith angles approaching $90^\circ$), with measured reflectivity for those events which exceeded extant calculations. The HiCal-2 experiment, flying from Dec., 2016-Jan., 2017, provided an improvement by nearly two orders of magnitude in our event statistics, allowing a considerably more precise mapping of the reflectivity over a wider range of incidence angles. We find general agreement between the HiCal-2 reflectivity results and those obtained with the earlier HiCal-1 mission, as well as estimates from Solar reflections in the radio-frequency regime. In parallel, our calculations of expected reflectivity have matured; herein, we use a plane-wave expansion to estimate the reflectivity R from both a flat, smooth surface (and, in so doing, recover the Fresnel reflectivity equations) and also a curved surface. Multiplying our flat-smooth reflectivity by improved Earth curvature and surface roughness corrections now provides significantly better agreement between theory and the HiCal 2a/2b measurements., Comment: submitted to Astropart. Phys

Published
2018
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49. Publisher Correction: Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus

Author

Kraemer, Moritz UG, Reiner, Robert C, Brady, Oliver J, Messina, Jane P, Gilbert, Marius, Pigott, David M, Yi, Dingdong, Johnson, Kimberly, Earl, Lucas, Marczak, Laurie B, Shirude, Shreya, Weaver, Nicole Davis, Bisanzio, Donal, Perkins, T Alex, Lai, Shengjie, Lu, Xin, Jones, Peter, Coelho, Giovanini E, Carvalho, Roberta G, Van Bortel, Wim, Marsboom, Cedric, Hendrickx, Guy, Schaffner, Francis, Moore, Chester G, Nax, Heinrich H, Bengtsson, Linus, Wetter, Erik, Tatem, Andrew J, Brownstein, John S, Smith, David L, Lambrechts, Louis, Cauchemez, Simon, Linard, Catherine, Faria, Nuno R, Pybus, Oliver G, Scott, Thomas W, Liu, Qiyong, Yu, Hongjie, Wint, GR William, Hay, Simon I, and Golding, Nick

Subjects
Microbiology, Biological Sciences, Prevention, Good Health and Well Being, Medical Microbiology
Abstract

In the version of this Article originally published, the affiliation for author Catherine Linard was incorrectly stated as '6Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK'. The correct affiliation is '9Spatial Epidemiology Lab (SpELL), Universite Libre de Bruxelles, Brussels, Belgium'. The affiliation for author Hongjie Yu was also incorrectly stated as '11Department of Statistics, Harvard University, Cambridge, MA, USA'. The correct affiliation is '15School of Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China'. This has now been amended in all versions of the Article.

Published
2019

50. Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus

Author

Kraemer, Moritz UG, Reiner, Robert C, Brady, Oliver J, Messina, Jane P, Gilbert, Marius, Pigott, David M, Yi, Dingdong, Johnson, Kimberly, Earl, Lucas, Marczak, Laurie B, Shirude, Shreya, Davis Weaver, Nicole, Bisanzio, Donal, Perkins, T Alex, Lai, Shengjie, Lu, Xin, Jones, Peter, Coelho, Giovanini E, Carvalho, Roberta G, Van Bortel, Wim, Marsboom, Cedric, Hendrickx, Guy, Schaffner, Francis, Moore, Chester G, Nax, Heinrich H, Bengtsson, Linus, Wetter, Erik, Tatem, Andrew J, Brownstein, John S, Smith, David L, Lambrechts, Louis, Cauchemez, Simon, Linard, Catherine, Faria, Nuno R, Pybus, Oliver G, Scott, Thomas W, Liu, Qiyong, Yu, Hongjie, Wint, GR William, Hay, Simon I, and Golding, Nick

Subjects
Microbiology, Biological Sciences, Rare Diseases, Vaccine Related, Prevention, Infectious Diseases, Emerging Infectious Diseases, Biodefense, Vector-Borne Diseases, Infection, Good Health and Well Being, Aedes, Animals, Arbovirus Infections, Arboviruses, Female, Humans, Mosquito Vectors, Medical Microbiology
Abstract

The global population at risk from mosquito-borne diseases-including dengue, yellow fever, chikungunya and Zika-is expanding in concert with changes in the distribution of two key vectors: Aedes aegypti and Aedes albopictus. The distribution of these species is largely driven by both human movement and the presence of suitable climate. Using statistical mapping techniques, we show that human movement patterns explain the spread of both species in Europe and the United States following their introduction. We find that the spread of Ae. aegypti is characterized by long distance importations, while Ae. albopictus has expanded more along the fringes of its distribution. We describe these processes and predict the future distributions of both species in response to accelerating urbanization, connectivity and climate change. Global surveillance and control efforts that aim to mitigate the spread of chikungunya, dengue, yellow fever and Zika viruses must consider the so far unabated spread of these mosquitos. Our maps and predictions offer an opportunity to strategically target surveillance and control programmes and thereby augment efforts to reduce arbovirus burden in human populations globally.

Published
2019

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