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401 results on '"Wakefield, Jon"'

7. Global, regional, and national trends in under-5 mortality between 1990 and 2019 with scenario-based projections until 2030: a systematic analysis by the UN Inter-agency Group for Child Mortality Estimation

Author

Sharrow, David, Hug, Lucia, You, Danzhen, Alkema, Leontine, Black, Robert, Cousens, Simon, Croft, Trevor, Gaigbe-Togbe, Victor, Gerland, Patrick, Guillot, Michel, Hill, Kenneth, Masquelier, Bruno, Mathers, Colin, Pedersen, Jon, Strong, Kathleen L, Suzuki, Emi, Wakefield, Jon, and Walker, Neff

Published
2022
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8. Modelling urban/rural fractions in low- and middle-income countries.

Author

Wu, Yunhan and Wakefield, Jon

Subjects
MIDDLE-income countries, REGRESSION trees, CLASSIFICATION algorithms, LOGISTIC regression analysis, HOUSEHOLD surveys, CLUSTER sampling
Abstract

In low- and middle-income countries, household surveys are the most reliable data source to examine health and demographic indicators at the subnational level, an exercise in small area estimation. Model-based unit-level models are favoured for producing the subnational estimates at fine scale, such as the admin-2 level. Typically, the surveys employ stratified 2-stage cluster sampling with strata consisting of an urban/rural designation crossed with administrative regions. To avoid bias and increase predictive precision, the stratification should be acknowledged in the analysis. To move from the cluster to the area requires an aggregation step in which the prevalence surface is averaged with respect to population density. This requires estimating a partition of the study area into its urban and rural components, and to do this we experiment with a variety of classification algorithms, including logistic regression, Bayesian additive regression trees, and gradient boosted trees. Pixel-level covariate surfaces are used to improve prediction. We estimate spatial HIV prevalence in women of age 15–49 in Malawi using the stratification/aggregation method we propose. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Naomi: a new modelling tool for estimating HIV epidemic indicators at the district level in sub-Saharan Africa

Author

Eaton, Jeffrey W., Dwyer-Lindgren, Laura, Gutreuter, Steve, O'Driscoll, Megan, Stevens, Oliver, Bajaj, Sumali, Ashton, Rob, Hill, Alexandra, Russell, Emma, Esra, Rachel, Dolan, Nicolas, Anifowoshe, Yusuf O., Woodbridge, Mark, Fellows, Ian, Glaubius, Robert, Haeuser, Emily, Okonek, Taylor, Stover, John, Thomas, Matthew L., Wakefield, Jon, Wolock, Timothy M., Berry, Jonathan, Sabala, Tomasz, Heard, Nathan, Delgado, Stephen, Jahn, Andreas, Kalua, Thokozani, Chimpandule, Tiwonge, Auld, Andrew, Kim, Evelyn, Payne, Danielle, Johnson, Leigh F., FitzJohn, Richard G., Wanyeki, Ian, Mahy, Mary I., and Shiraishi, Ray W.

Subjects
Statistical models -- Methods -- Evaluation, Epidemics -- Models -- Forecasts and trends -- Sub-Saharan Africa, HIV infection -- Models -- Forecasts and trends -- Distribution, Market trend/market analysis, Company distribution practices, Health
Abstract

Introduction: HIV planning requires granular estimates for the number of people living with HIV (PLHIV), antiretroviral treatment (ART) coverage and unmet need, and new HIV infections by district, or equivalent subnational administrative level. We developed a Bayesian small-area estimation model, called Naomi, to estimate these quantities stratified by subnational administrative units, sex, and five-year age groups. Methods: Small-area regressions for HIV prevalence, ART coverage and HIV incidence were jointly calibrated using subnational household survey data on all three indicators, routine antenatal service delivery data on HIV prevalence and ART coverage among pregnant women, and service delivery data on the number of PLHIV receiving ART. Incidence was modelled by district-level HIV prevalence and ART coverage. Model outputs of counts and rates for each indicator were aggregated to multiple geographic and demographic stratifications of interest. The model was estimated in an empirical Bayes framework, furnishing probabilistic uncertainty ranges for all output indicators. Example results were presented using data from Malawi during 2016-2018. Results: Adult HIV prevalence in September 2018 ranged from 3.2% to 17.1% across Malawi's districts and was higher in southern districts and in metropolitan areas. ART coverage was more homogenous, ranging from 75% to 82%. The largest number of PLHIV was among ages 35 to 39 for both women and men, while the most untreated PLHIV were among ages 25 to 29 for women and 30 to 34 for men. Relative uncertainty was larger for the untreated PLHIV than the number on ART or total PLHIV. Among clients receiving ART at facilities in Lilongwe city, an estimated 71% (95% CI, 61% to 79%) resided in Lilongwe city, 20% (14% to 27%) in Lilongwe district outside the metropolis, and 9% (6% to 12%) in neighbouring Dowa district. Thirty-eight percent (26% to 50%) of Lilongwe rural residents and 39% (27% to 50%) of Dowa residents received treatment at facilities in Lilongwe city. Conclusions: The Naomi model synthesizes multiple subnational data sources to furnish estimates of key indicators for HIV programme planning, resource allocation, and target setting. Further model development to meet evolving HIV policy priorities and programme need should be accompanied by continued strengthening and understanding of routine health system data. Keywords: Bayesian statistics; HIV estimates; joint modelling; routine data; small-area estimation, 1 | INTRODUCTION The global community has established ambitious goals for expanding coverage of antiretroviral treatment (ART) and reducing new HIV infections to end AIDS as a public health threat [...]

Published
2021
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10. Global, regional, and national estimates and trends in stillbirths from 2000 to 2019: a systematic assessment

Author

Hug, Lucia, You, Danzhen, Blencowe, Hannah, Mishra, Anu, Wang, Zhengfan, Fix, Miranda J, Wakefield, Jon, Moran, Allisyn C, Gaigbe-Togbe, Victor, Suzuki, Emi, Blau, Dianna M, Cousens, Simon, Creanga, Andreea, Croft, Trevor, Hill, Kenneth, Joseph, K S, Maswime, Salome, McClure, Elizabeth M, Pattinson, Robert, Pedersen, Jon, Smith, Lucy K, Zeitlin, Jennifer, and Alkema, Leontine

Published
2021
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12. A Computationally Efficient Approach to Fully Bayesian Benchmarking.

Author

Okonek, Taylor and Wakefield, Jon

Subjects
*SMALL area statistics, *BENCHMARKING (Management), *MIDDLE-income countries
Abstract

In small area estimation, it is sometimes necessary to use model-based methods to produce estimates in areas with little or no data. In official statistics, we often require that aggregates of small area estimates agree with national estimates for internal consistency purposes. Enforcing this agreement is referred to as benchmarking, and while methods currently exist to perform benchmarking, few are ideal for applications with non-normal outcomes and benchmarks with uncertainty. Fully Bayesian benchmarking is a theoretically appealing approach insofar as we can obtain posterior distributions conditional on a benchmarking constraint. However, existing implementations may be computationally prohibitive. In this paper, we critically review benchmarking methods in the context of small area estimation in low- and middle-income countries with binary outcomes and uncertain benchmarks, and propose a novel approach in which posterior samples of small area characteristics from an unbenchmarked model can be combined with a rejection sampler or Metropolis-Hastings algorithm to produce benchmarked posterior distributions in a computationally efficient way. To illustrate the flexibility and efficiency of our approach, we provide comparisons to an existing benchmarking approach in a simulation, and applications to HIV prevalence and under-5 mortality estimation. Code implementing our methodology is available in the R package stbench. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Smoothed model‐assisted small area estimation of proportions.

Author

Gao, Peter A. and Wakefield, Jon

Subjects
*GEOLOGICAL statistics, *CENSUS, *HEALTH status indicators
Abstract

In countries where population census data are limited, generating accurate subnational estimates of health and demographic indicators is challenging. Existing model‐based geostatistical methods leverage covariate information and spatial smoothing to reduce the variability of estimates but often ignore the survey design, while traditional small area estimation approaches may not incorporate both unit‐level covariate information and spatial smoothing in a design consistent way. We propose a smoothed model‐assisted estimator that accounts for survey design and leverages both unit‐level covariates and spatial smoothing. Under certain regularity assumptions, this estimator is both design consistent and model consistent. We compare it with existing design‐based and model‐based estimators using real and simulated data. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Incorporating testing volume into estimation of effective reproduction number dynamics.

Author

Goldstein, Isaac H, Wakefield, Jon, and Minin, Volodymyr M

Subjects
COVID-19 pandemic, BRANCHING processes, COMMUNICABLE diseases, DISEASE outbreaks, DIAGNOSIS methods
Abstract

Branching process inspired models are widely used to estimate the effective reproduction number—a useful summary statistic describing an infectious disease outbreak—using counts of new cases. Case data is a real-time indicator of changes in the reproduction number, but is challenging to work with because cases fluctuate due to factors unrelated to the number of new infections. We develop a new model that incorporates the number of diagnostic tests as a surveillance model covariate. Using simulated data and data from the SARS-CoV-2 pandemic in California, we demonstrate that incorporating tests leads to improved performance over the state of the art. [ABSTRACT FROM AUTHOR]

Published
2024
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15. Global, Regional, and National Estimates and Trends in Stillbirths From 2000 to 2019: A Systematic Assessment

Author

Hug, Lucia, You, Danzhen, Blencowe, Hannah, Mishra, Anu, Wang, Zhengfan, Fix, Miranda J., Wakefield, Jon, Moran, Allisyn C., Gaigbe-Togbe, Victor, Suzuki, Emi, Blau, Dianna M., Cousens, Simon, Creanga, Andreea, Croft, Trevor, Hill, Kenneth, Joseph, K. S., Maswime, Salome, McClure, Elizabeth M., Pattinson, Robert, Pedersen, Jon, Smith, Lucy K., Zeitlin, Jennifer, and Alkema, Leontine

Published
2022
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16. Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Author

Burstein, Roy, Henry, Nathaniel J., Collison, Michael L., Marczak, Laurie B., Sligar, Amber, Watson, Stefanie, Marquez, Neal, Abbasalizad-Farhangi, Mahdieh, Abbasi, Masoumeh, Abd-Allah, Foad, Abdoli, Amir, Abdollahi, Mohammad, Abdollahpour, Ibrahim, Abdulkader, Rizwan Suliankatchi, Abrigo, Michael R. M., Acharya, Dilaram, Adebayo, Oladimeji M., Adekanmbi, Victor, Adham, Davoud, Afshari, Mahdi, Aghaali, Mohammad, Ahmadi, Keivan, Ahmadi, Mehdi, Ahmadpour, Ehsan, Ahmed, Rushdia, Akal, Chalachew Genet, Akinyemi, Joshua O., Alahdab, Fares, Alam, Noore, Alamene, Genet Melak, Alene, Kefyalew Addis, Alijanzadeh, Mehran, Alinia, Cyrus, Alipour, Vahid, Aljunid, Syed Mohamed, Almalki, Mohammed J., Al-Mekhlafi, Hesham M., Altirkawi, Khalid, Alvis-Guzman, Nelson, Amegah, Adeladza Kofi, Amini, Saeed, Amit, Arianna Maever Loreche, Anbari, Zohreh, Androudi, Sofia, Anjomshoa, Mina, Ansari, Fereshteh, Antonio, Carl Abelardo T., Arabloo, Jalal, Arefi, Zohreh, Aremu, Olatunde, Armoon, Bahram, Arora, Amit, Artaman, Al, Asadi, Anvar, Asadi-Aliabadi, Mehran, Ashraf-Ganjouei, Amir, Assadi, Reza, Ataeinia, Bahar, Atre, Sachin R., Quintanilla, Beatriz Paulina Ayala, Ayanore, Martin Amogre, Azari, Samad, Babaee, Ebrahim, Babazadeh, Arefeh, Badawi, Alaa, Bagheri, Soghra, Bagherzadeh, Mojtaba, Baheiraei, Nafiseh, Balouchi, Abbas, Barac, Aleksandra, Bassat, Quique, Baune, Bernhard T., Bayati, Mohsen, Bedi, Neeraj, Beghi, Ettore, Behzadifar, Masoud, Behzadifar, Meysam, Belay, Yared Belete, Bell, Brent, Bell, Michelle L., Berbada, Dessalegn Ajema, Bernstein, Robert S., Bhattacharjee, Natalia V., Bhattarai, Suraj, Bhutta, Zulfiqar A., Bijani, Ali, Bohlouli, Somayeh, Breitborde, Nicholas J. K., Britton, Gabrielle, Browne, Annie J., Nagaraja, Sharath Burugina, Busse, Reinhard, Butt, Zahid A., Car, Josip, Cárdenas, Rosario, Castañeda-Orjuela, Carlos A., Cerin, Ester, Chanie, Wagaye Fentahun, Chatterjee, Pranab, Chu, Dinh-Toi, Cooper, Cyrus, Costa, Vera M., Dalal, Koustuv, Dandona, Lalit, Dandona, Rakhi, Daoud, Farah, Daryani, Ahmad, Das Gupta, Rajat, Davis, Ian, Davis Weaver, Nicole, Davitoiu, Dragos Virgil, De Neve, Jan-Walter, Demeke, Feleke Mekonnen, Demoz, Gebre Teklemariam, Deribe, Kebede, Desai, Rupak, Deshpande, Aniruddha, Desyibelew, Hanna Demelash, Dey, Sagnik, Dharmaratne, Samath Dhamminda, Dhimal, Meghnath, Diaz, Daniel, Doshmangir, Leila, Duraes, Andre R., Dwyer-Lindgren, Laura, Earl, Lucas, Ebrahimi, Roya, Ebrahimpour, Soheil, Effiong, Andem, Eftekhari, Aziz, Ehsani-Chimeh, Elham, El Sayed, Iman, El Sayed Zaki, Maysaa, El Tantawi, Maha, El-Khatib, Ziad, Emamian, Mohammad Hassan, Enany, Shymaa, Eskandarieh, Sharareh, Eyawo, Oghenowede, Ezalarab, Maha, Faramarzi, Mahbobeh, Fareed, Mohammad, Faridnia, Roghiyeh, Faro, Andre, Fazaeli, Ali Akbar, Fazlzadeh, Mehdi, Fentahun, Netsanet, Fereshtehnejad, Seyed-Mohammad, Fernandes, João C., Filip, Irina, Fischer, Florian, Foigt, Nataliya A., Foroutan, Masoud, Francis, Joel Msafiri, Fukumoto, Takeshi, Fullman, Nancy, Gallus, Silvano, Gebre, Destallem Gebremedhin, Gebrehiwot, Tsegaye Tewelde, Gebremeskel, Gebreamlak Gebremedhn, Gessner, Bradford D., Geta, Birhanu, Gething, Peter W., Ghadimi, Reza, Ghadiri, Keyghobad, Ghajarzadeh, Mahsa, Ghashghaee, Ahmad, Gill, Paramjit Singh, Gill, Tiffany K., Golding, Nick, Gomes, Nelson G. M., Gona, Philimon N., Gopalani, Sameer Vali, Gorini, Giuseppe, Goulart, Bárbara Niegia Garcia, Graetz, Nicholas, Greaves, Felix, Green, Manfred S., Guo, Yuming, Haj-Mirzaian, Arvin, Haj-Mirzaian, Arya, Hall, Brian James, Hamidi, Samer, Haririan, Hamidreza, Haro, Josep Maria, Hasankhani, Milad, Hasanpoor, Edris, Hasanzadeh, Amir, Hassankhani, Hadi, Hassen, Hamid Yimam, Hegazy, Mohamed I., Hendrie, Delia, Heydarpour, Fatemeh, Hird, Thomas R., Hoang, Chi Linh, Hollerich, Gillian, Rad, Enayatollah Homaie, Hoseini-Ghahfarokhi, Mojtaba, Hossain, Naznin, Hosseini, Mostafa, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Househ, Mowafa, Hsairi, Mohamed, Ilesanmi, Olayinka Stephen, Imani-Nasab, Mohammad Hasan, Iqbal, Usman, Irvani, Seyed Sina Naghibi, Islam, Nazrul, Islam, Sheikh Mohammed Shariful, Jürisson, Mikk, Balalami, Nader Jafari, Jalali, Amir, Javidnia, Javad, Jayatilleke, Achala Upendra, Jenabi, Ensiyeh, Ji, John S., Jobanputra, Yash B., Johnson, Kimberly, Jonas, Jost B., Shushtari, Zahra Jorjoran, Jozwiak, Jacek Jerzy, Kabir, Ali, Kahsay, Amaha, Kalani, Hamed, Kalhor, Rohollah, Karami, Manoochehr, Karki, Surendra, Kasaeian, Amir, Kassebaum, Nicholas J., Keiyoro, Peter Njenga, Kemp, Grant Rodgers, Khabiri, Roghayeh, Khader, Yousef Saleh, Khafaie, Morteza Abdullatif, Khan, Ejaz Ahmad, Khan, Junaid, Khan, Muhammad Shahzeb, Khang, Young-Ho, Khatab, Khaled, Khater, Amir, Khater, Mona M., Khatony, Alireza, Khazaei, Mohammad, Khazaei, Salman, Khazaei-Pool, Maryam, Khubchandani, Jagdish, Kianipour, Neda, Kim, Yun Jin, Kimokoti, Ruth W., Kinyoki, Damaris K., Kisa, Adnan, Kisa, Sezer, Kolola, Tufa, Kosen, Soewarta, Koul, Parvaiz A., Koyanagi, Ai, Kraemer, Moritz U. G., Krishan, Kewal, Krohn, Kris J., Kugbey, Nuworza, Kumar, G. Anil, Kumar, Manasi, Kumar, Pushpendra, Kuupiel, Desmond, Lacey, Ben, Lad, Sheetal D., Lami, Faris Hasan, Larsson, Anders O., Lee, Paul H., Leili, Mostafa, Levine, Aubrey J., Li, Shanshan, Lim, Lee-Ling, Listl, Stefan, Longbottom, Joshua, Lopez, Jaifred Christian F., Lorkowski, Stefan, Magdeldin, Sameh, Abd El Razek, Hassan Magdy, Abd El Razek, Muhammed Magdy, Majeed, Azeem, Maleki, Afshin, Malekzadeh, Reza, Malta, Deborah Carvalho, Mamun, Abdullah A., Manafi, Navid, Manda, Ana-Laura, Mansourian, Morteza, Martins-Melo, Francisco Rogerlândio, Masaka, Anthony, Massenburg, Benjamin Ballard, Maulik, Pallab K., Mayala, Benjamin K., Mazidi, Mohsen, McKee, Martin, Mehrotra, Ravi, Mehta, Kala M., Meles, Gebrekiros Gebremichael, Mendoza, Walter, Menezes, Ritesh G., Meretoja, Atte, Meretoja, Tuomo J., Mestrovic, Tomislav, Miller, Ted R., Miller-Petrie, Molly K., Mills, Edward J., Milne, George J., Mini, G. K., Mir, Seyed Mostafa, Mirjalali, Hamed, Mirrakhimov, Erkin M., Mohamadi, Efat, Mohammad, Dara K., Darwesh, Aso Mohammad, Mezerji, Naser Mohammad Gholi, Mohammed, Ammas Siraj, Mohammed, Shafiu, Mokdad, Ali H., Molokhia, Mariam, Monasta, Lorenzo, Moodley, Yoshan, Moosazadeh, Mahmood, Moradi, Ghobad, Moradi, Masoud, Moradi, Yousef, Moradi-Lakeh, Maziar, Moradinazar, Mehdi, Moraga, Paula, Morawska, Lidia, Mosapour, Abbas, Mousavi, Seyyed Meysam, Mueller, Ulrich Otto, Muluneh, Atalay Goshu, Mustafa, Ghulam, Nabavizadeh, Behnam, Naderi, Mehdi, Nagarajan, Ahamarshan Jayaraman, Nahvijou, Azin, Najafi, Farid, Nangia, Vinay, Ndwandwe, Duduzile Edith, Neamati, Nahid, Negoi, Ionut, Negoi, Ruxandra Irina, Ngunjiri, Josephine W., Thi Nguyen, Huong Lan, Nguyen, Long Hoang, Nguyen, Son Hoang, Nielsen, Katie R., Ningrum, Dina Nur Anggraini, Nirayo, Yirga Legesse, Nixon, Molly R., Nnaji, Chukwudi A., Nojomi, Marzieh, Noroozi, Mehdi, Nosratnejad, Shirin, Noubiap, Jean Jacques, Motlagh, Soraya Nouraei, Ofori-Asenso, Richard, Ogbo, Felix Akpojene, Oladimeji, Kelechi E., Olagunju, Andrew T., Olfatifar, Meysam, Olum, Solomon, Olusanya, Bolajoko Olubukunola, Oluwasanu, Mojisola Morenike, Onwujekwe, Obinna E., Oren, Eyal, Ortega-Altamirano, Doris D. V., Ortiz, Alberto, Osarenotor, Osayomwanbo, Osei, Frank B., Osgood-Zimmerman, Aaron E., Otstavnov, Stanislav S., Owolabi, Mayowa Ojo, P. A., Mahesh, Pagheh, Abdol Sattar, Pakhale, Smita, Panda-Jonas, Songhomitra, Pandey, Animika, Park, Eun-Kee, Parsian, Hadi, Pashaei, Tahereh, Patel, Sangram Kishor, Pepito, Veincent Christian Filipino, Pereira, Alexandre, Perkins, Samantha, Pickering, Brandon V., Pilgrim, Thomas, Pirestani, Majid, Piroozi, Bakhtiar, Pirsaheb, Meghdad, Plana-Ripoll, Oleguer, Pourjafar, Hadi, Puri, Parul, Qorbani, Mostafa, Quintana, Hedley, Rabiee, Mohammad, Rabiee, Navid, Radfar, Amir, Rafiei, Alireza, Rahim, Fakher, Rahimi, Zohreh, Rahimi-Movaghar, Vafa, Rahimzadeh, Shadi, Rajati, Fatemeh, Raju, Sree Bhushan, Ramezankhani, Azra, Ranabhat, Chhabi Lal, Rasella, Davide, Rashedi, Vahid, Rawal, Lal, Reiner Jr, Robert C., Renzaho, Andre M. N., Rezaei, Satar, Rezapour, Aziz, Riahi, Seyed Mohammad, Ribeiro, Ana Isabel, Roever, Leonardo, Roro, Elias Merdassa, Roser, Max, Roshandel, Gholamreza, Roshani, Daem, Rostami, Ali, Rubagotti, Enrico, Rubino, Salvatore, Sabour, Siamak, Sadat, Nafis, Sadeghi, Ehsan, Saeedi, Reza, Safari, Yahya, Safari-Faramani, Roya, Safdarian, Mahdi, Sahebkar, Amirhossein, Salahshoor, Mohammad Reza, Salam, Nasir, Salamati, Payman, Salehi, Farkhonde, Zahabi, Saleh Salehi, Salimi, Yahya, Salimzadeh, Hamideh, Salomon, Joshua A., Sambala, Evanson Zondani, Samy, Abdallah M., Santric Milicevic, Milena M., Jose, Bruno Piassi Sao, Saraswathy, Sivan Yegnanarayana Iyer, Sarmiento-Suárez, Rodrigo, Sartorius, Benn, Sathian, Brijesh, Saxena, Sonia, Sbarra, Alyssa N., Schaeffer, Lauren E., Schwebel, David C., Sepanlou, Sadaf G., Seyedmousavi, Seyedmojtaba, Shaahmadi, Faramarz, Shaikh, Masood Ali, Shams-Beyranvand, Mehran, Shamshirian, Amir, Shamsizadeh, Morteza, Sharafi, Kiomars, Sharif, Mehdi, Sharif-Alhoseini, Mahdi, Sharifi, Hamid, Sharma, Jayendra, Sharma, Rajesh, Sheikh, Aziz, Shields, Chloe, Shigematsu, Mika, Shiri, Rahman, Shiue, Ivy, Shuval, Kerem, Siddiqi, Tariq J., Silva, João Pedro, Singh, Jasvinder A., Sinha, Dhirendra Narain, Sisay, Malede Mequanent, Sisay, Solomon, Sliwa, Karen, Smith, David L., Somayaji, Ranjani, Soofi, Moslem, Soriano, Joan B., Sreeramareddy, Chandrashekhar T., Sudaryanto, Agus, Sufiyan, Mu’awiyyah Babale, Sykes, Bryan L., Sylaja, P. N., Tabarés-Seisdedos, Rafael, Tabb, Karen M., Tabuchi, Takahiro, Taveira, Nuno, Temsah, Mohamad-Hani, Terkawi, Abdullah Sulieman, Tessema, Zemenu Tadesse, Thankappan, Kavumpurathu Raman, Thirunavukkarasu, Sathish, To, Quyen G., Tovani-Palone, Marcos Roberto, Tran, Bach Xuan, Tran, Khanh Bao, Ullah, Irfan, Usman, Muhammad Shariq, Uthman, Olalekan A., Vahedian-Azimi, Amir, Valdez, Pascual R., van Boven, Job F. M., Vasankari, Tommi Juhani, Vasseghian, Yasser, Veisani, Yousef, Venketasubramanian, Narayanaswamy, Violante, Francesco S., Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vos, Theo, Vu, Giang Thu, Vujcic, Isidora S., Waheed, Yasir, Wakefield, Jon, Wang, Haidong, Wang, Yafeng, Wang, Yuan-Pang, Ward, Joseph L., Weintraub, Robert G., Weldegwergs, Kidu Gidey, Weldesamuel, Girmay Teklay, Westerman, Ronny, Wiysonge, Charles Shey, Wondafrash, Dawit Zewdu, Woyczynski, Lauren, Wu, Ai-Min, Xu, Gelin, Yadegar, Abbas, Yamada, Tomohide, Yazdi-Feyzabadi, Vahid, Yilgwan, Christopher Sabo, Yip, Paul, Yonemoto, Naohiro, Lebni, Javad Yoosefi, Younis, Mustafa Z., Yousefifard, Mahmoud, Yousof, Hebat-Allah Salah A., Yu, Chuanhua, Yusefzadeh, Hasan, Zabeh, Erfan, Moghadam, Telma Zahirian, Bin Zaman, Sojib, Zamani, Mohammad, Zandian, Hamed, Zangeneh, Alireza, Zerfu, Taddese Alemu, Zhang, Yunquan, Ziapour, Arash, Zodpey, Sanjay, Murray, Christopher J. L., and Hay, Simon I.

Published
2019
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17. Rejoinder to the discussion on "The central role of the identifying assumption in population size estimation".

Author

Aleshin-Guendel, Serge, Sadinle, Mauricio, and Wakefield, Jon

Subjects
SENSITIVITY analysis
Abstract

We organize the discussants' major comments into the following categories: sensitivity analyses, zero counts, model selection, the marginal no-highest-order interaction (NHOI) assumption, and the usefulness of our proposed framework. [ABSTRACT FROM AUTHOR]

Published
2024
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18. The central role of the identifying assumption in population size estimation.

Author

Aleshin-Guendel, Serge, Sadinle, Mauricio, and Wakefield, Jon

Subjects
CIVILIAN war casualties, MISSING data (Statistics), SENSITIVITY analysis
Abstract

The problem of estimating the size of a population based on a subset of individuals observed across multiple data sources is often referred to as capture-recapture or multiple-systems estimation. This is fundamentally a missing data problem, where the number of unobserved individuals represents the missing data. As with any missing data problem, multiple-systems estimation requires users to make an untestable identifying assumption in order to estimate the population size from the observed data. If an appropriate identifying assumption cannot be found for a data set, no estimate of the population size should be produced based on that data set, as models with different identifying assumptions can produce arbitrarily different population size estimates—even with identical observed data fits. Approaches to multiple-systems estimation often do not explicitly specify identifying assumptions. This makes it difficult to decouple the specification of the model for the observed data from the identifying assumption and to provide justification for the identifying assumption. We present a re-framing of the multiple-systems estimation problem that leads to an approach that decouples the specification of the observed-data model from the identifying assumption, and discuss how common models fit into this framing. This approach takes advantage of existing software and facilitates various sensitivity analyses. We demonstrate our approach in a case study estimating the number of civilian casualties in the Kosovo war. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Cotton Gin Stand Machine-Vision Inspection and Removal System for Plastic Contamination: Hand Intrusion Sensor Design.

Author

Pelletier, Mathew G., Wanjura, John D., Wakefield, Jon R., Holt, Greg A., and Kothari, Neha

Subjects
COTTON gins & ginning, COTTON quality, PLASTICS, COTTON textiles, COTTON trade, GIN industry, DETECTORS
Abstract

Plastic contamination in cotton lint poses significant challenges to the U.S. cotton industry, with plastic wrap from John Deere round module harvesters being a primary contaminant. Despite efforts to manually remove this plastic during module unwrapping, some inevitably enters the cotton gin's processing system. To address this, a machine-vision detection and removal system has been developed. This system uses inexpensive color cameras to identify plastic on the gin stand feeder apron, triggering a mechanism that expels the plastic from the cotton stream. However, the system, composed of 30–50 Linux-based ARM computers, requires substantial effort for calibration and tuning and presents a technological barrier for typical cotton gin workers. This research aims to transition the system to a more user-friendly, plug-and-play model by implementing an auto-calibration function. The proposed function dynamically tracks cotton colors while excluding plastic images that could hinder performance. A critical component of this auto-calibration algorithm is the hand intrusion detector, or "HID", which is discussed in this paper. In the normal operation of a cotton gin, the gin personnel periodically have to clear the machine, which entails running a stick or their arm/hand under the detection cameras. This results in the system capturing a false positive, which interferes with the ability of auto-calibration algorithms to function correctly. Hence, there is a critical need for an HID to remove these false positives from the record. The anticipated benefits of the auto-calibration function include reduced setup and maintenance overhead, less reliance on skilled personnel, and enhanced adoption of the plastic removal system within the cotton ginning industry. [ABSTRACT FROM AUTHOR]

Published
2024
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21. From start to finish : a framework for the production of small area official statistics

Author

Tzavidis, Nikos, Zhang, Li-Chun, Luna, Angela, Schmid, Timo, Rojas-Perilla, Natalia, Gordon, lan R., Williamson, Paul, King, Thomas, Ranalli, Maria Giovanna, Smith, Paul A., Matz, David, Baffour, Bernard, Thall, Peter F., Pfeffermann, Danny, Sperlich, Stefan, Fabrizi, Enrico, Kumar, Kuldeep, Dorfman, Alan H., Falorsi, Stefano, Bell, William R., Fuglstad, Geir-Arne, Riebler, Andrea, Wakefield, Jon, Paige, Johnny, Wilson, Katie, Dong, Tracy, Kim, Seongho, Wong, Weng Kee, Münnich, Ralf, Szymkowiak, Marcin, Li, Zehang Richard, Hsiao, Yuan, Martin, Bryan D., Godwin, Jessica, Clark, Sam J., and Zimmermann, Thomas

Published
2018

38. A Penalized Poisson Likelihood Approach to High-Dimensional Semi-Parametric Inference for Doubly-Stochastic Point Processes

Author

Cheng, Si, Wakefield, Jon, and Shojaie, Ali

Subjects
Methodology (stat.ME), FOS: Computer and information sciences, Statistics - Machine Learning, Machine Learning (stat.ML), Statistics - Computation, Computation (stat.CO), Statistics - Methodology
Abstract

Doubly-stochastic point processes model the occurrence of events over a spatial domain as an inhomogeneous Poisson process conditioned on the realization of a random intensity function. They are flexible tools for capturing spatial heterogeneity and dependence. However, implementations of doubly-stochastic spatial models are computationally demanding, often have limited theoretical guarantee, and/or rely on restrictive assumptions. We propose a penalized regression method for estimating covariate effects in doubly-stochastic point processes that is computationally efficient and does not require a parametric form or stationarity of the underlying intensity. We establish the consistency and asymptotic normality of the proposed estimator, and develop a covariance estimator that leads to a conservative statistical inference procedure. A simulation study shows the validity of our approach under less restrictive assumptions on the data generating mechanism, and an application to Seattle crime data demonstrates better prediction accuracy compared with existing alternatives.

Published
2023

41. Geogenomic Segregation and Temporal Trends of Human Pathogenic Escherichia coli O157:H7, Washington, USA, 2005-2014

Author

Tarr, Gillian A.M., Shringi, Smriti, Phipps, Amanda I., Besser, Thomas E., Mayer, Jonathan, Oltean, Hanna N., Wakefield, Jon, Tarr, Phillip I., and Rabinowitz, Peter

Subjects
Infection -- Health aspects -- Analysis, Phylogeny -- Health aspects -- Analysis, Escherichia coli -- Health aspects -- Analysis, Health
Abstract

Escherichia coli O157:H7 infections cause major public health challenges. Most E. coli O157:H7 infections occur sporadically, and the source of infection is often difficult to identify with certainty (1,2). Many [...]

Published
2018
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47. Excavating Neandertal and Denisovan DNA from the genomes of Melanesian individuals

Author

Vernot, Benjamin, Tucci, Serena, Kelso, Janet, Schraiber, Joshua G., Wolf, Aaron B., Gittelman, Rachel M., Dannemann, Michael, Grote, Steffi, McCoy, Rajiv C., Norton, Heather, Scheinfeldt, Laura B., Merriwether, David A., Koki, George, Friedlaender, Jonathan S., Wakefield, Jon, Pääbo, Svante, and Akey, Joshua M.

Published
2016

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