#  ETHNO-DAANN: Ethnographic Engagement Classification by Deep Adversarial
Transfer Learning ††thanks: Thanks to support provided by Professor Dr Zuzana
Kubincova

Rossi Kamal, Zuzana Kubincova  Comenius University Bratislava
<EMAIL_ADDRESS>Mosaddek Hossain Kamal, Upama Kabir  University of
Dhaka
<EMAIL_ADDRESS>
###### Abstract

Student motivation is a key research agenda due to the necessity of both
‘postcolonial education reform’ and ‘youth job-market adaptation in ongoing
fourth industrial revolution’. Post-communism era teachers are prompted to
analyze students’ ethnicity information such as background, origin with the
aim of providing better education. With the proliferation of smart-device
data, ever-increasing demand for distance learning platforms and various
survey results of virtual learning, we are fortunate to have some access to
student engagement data. In this research, we are motivated to address the
following questions: can we predict student engagement from ethnographic
information when we have limited labeled knowledge? If the answer is yes, can
we tell which features are most influential in ethnographic engagement
learning? In this context, we have proposed a deep neural network based
transfer learning algorithm ETHNO-DAANN with adversarial adaptation for
ethnographic engagement prediction. We conduct a survey among participants
about ethnicity-based student motivation to figure out the most influential
feature helpful in final prediction. Thus, our research stands as a general
solution for ethnographic motivation parameter estimation in case of limited
labeled data.

###### Index Terms:

Student Motivation, Deep Transfer Learning, Ethnicity, Adversarial Training

##  I Introduction

Figure 1:  ETHNO-DAANN based Ethnographic Engagement Classification with Deep
Adversarial Transfer Learning

.

###  I-A Research Motivation

Figure 2:  Ethnographic Engagement Classification Scenario

.

Teachers in the post-communism era are social leaders, who are prompted to
discover inherent qualities of pupils/future citizens[1]-[6]. Furthermore,
students need to be‘ prepared for the ongoing job-market caused by the high
voltage fourth industrial revolution. Driven by such education reform, we are
interested in in-depth understanding of student background, origin, family
history to provide quality education to them. Especially, in post-colonial
countries,we observe an increasing number of foreign citizens, in addition to
existing homogeneous native and variant d ethnic groups. Time has come, when
urban policy makers are thinking to welcome and integrate diversification in
education curriculum.Fortunately, the presence of smart-device enabled
distance learning platforms provide access to student performance assessment
data. Network governance authority on local campuses also has access to
student statistics on an offline and online basis. However,datasets from these
two sources [on campus and off-campus] are not labeled with required knowledge
by which student genre, demography, ethnicity can be analyzed properly.
Machine learning or artificial intelligence is recognized for providing
quality assessment with statistical information. However, such a mechanism
requires abundant labeled knowledge , which is expensive, human labor-
intensive and time-consuming.

Transfer learning, a concept of cross-platform machine learning remedies this
problem by learning a target domain(i.e dataset) with the acquired knowledge
from source domain(i.e. dataset)[7][8]. This is applicable when we have less
amount of labeled knowledge in target domain.Transfer learning or domain
adaptation is a technique of cross-platform machine learning. Transfer
learning uses adversarial training to adjust balance in between source and
target domain. In this paradigm, a discriminative model is developed which
learns to remove any form of imbalance in between two domains. In this
mechanism, re weighting samples or even transforming both source and target in
the same feature space are common. Transfer learning or domain adaptation uses
single or multiple discriminator to adjust source and target domain. Based on
the similarity between domains, global or local distributions pay the price in
such adversarial training.WC In order to make the process a success, local or
global distributions of domain need to be carefuly considered. Sometimes it is
an outcome of global adversarial training or sometimes it is joint outcome of
multiple local classifiers.Deep convolutional neural networks learn
transferable features for domain adaptation by hierarchical abstraction
towards domain-independent feature representations.

Figure 3: ML versus transfer learning

.

We have proposed a two phase deep learning algorithm ETHNO-DAAN for
ethnographic engagement classification by using adversarial training and then
transfer learning .In the first phase, data inconsistency across domains is
considered in generating synthetic samples to discover latent mapping in
accessing under-represented data from over-represented samples. In the second
phase, we exploit deep convolutional neural networks for classifying
ethnographic information by intersecting original data and synthesized
samples. In such a learning process, the classifier tends to minimize the loss
function, so that deep representations are learnt from hardcore and easy
examples.

The overall organization of the paper is as follows (a)section-II includes
problem formulation, where we assess ethnographic engagement problem in post-
communist society, followed by with deep transfer learning based mathematical
modeling formulation, (b) section-III includes proposed classification
algorithm ETHNO-DAAN with theoretical justification, (c) section-IV includes
survey among participants to formulate hypotheses from key ethnographic
features, (d) we conclude our work with future directions in section V

##  II Problem Formulation

In this section, we illustrate ethnographic factors and their merits,
demerits, and variants towards student motivation in a post-colonial society.
We also provide mathematical modeling formulation towards ethnicity-driven
engagement classification.

###  II-A Need for Ethnographic Engagement

In this section, we describe the background of post-communist education reform
with ethnographic information[1]. Ethnicity based segmented knowledge is
proven to meet equal and individual demand of students[2][3]. However, such
kinds of curriculum is always built by considering the less-privileged
society[ 4][5].Furthermore, if not regulated or monitored properly, the
expected benefit from ethnographic segmentation is unachievable[6].

The post-colonial demands of education-reform stem from innovative social
theory. Eventually, teachers, who are raised with a closed-door or slavery
mindset, discover their different societal roles and thus intent to rejuvenate
ethnic groups and minorities.Ethnicity is a major role player in understanding
student motivation, which proliferates from culture, background of his own and
teacher.This is visible through student’s cooperation with pupils, his
expectations from teachers in solving assignment, task or even understanding
teachers’ behavior in merely collaboration scale, rather than written law.

Now-a-days campaigns for student motivation demand segmented
knowledge.Eventually, cross-cultural analysis of different communities and
groups are very important. Because, student performance increases when equal
and independent demands of pupils are given importance.However, post-colonial
people are less interested to learn from the past and more curious about
globalization.However, in order to develop education culture, past practice
and modern innovation are necessary to converge with the European flag.
Ethnicity driven curriculum also motivates students with learning outcomes of
different courses,especially social theory, linguistics. This brings social
activists or teachers closer with minority communities in helping them for
their development.

Education learnt at segmented ethnic schools helps students to some extent,
however, deprives them of globalization, as a result, students lose their
identify or socialization e, or, even socialization skills.Similarly,
ethnicity persuades students expressing their liking or disliking in a
stronger way.How the students are raised in what environment, determines how
they create small-sub-groups to fuel particular feelings against class or
teacher, which often unfortunately leads towards loss of faith in schooling. 6

Ethnicity is regarded as a media for identifying and reconciling education
inequalities. This requires defining discrimination and inequalities across
communities in a proper manner. Because, ethnicity can be realized differently
across various user-groups/citizens. As an example, we refer to stories of the
Roma community, who are less advanced than Ukraine citizens, thereby pedagogy,
learning materials are developed by considering relatively less advanced
communities.

###  II-B Mathematical Model Formulation

Lets discuss the preliminary notations of transfer learning, deep transfer
learning , followed by justification how the ethnic engagement classification
problem is amenable to unsupervised learning, domain adaptation, deep neural
network, respectively.

Figure 4: Typical steps in transfer Learning

.

Definition 2.1:$(D_{T},T_{T})$ be target learning domain and task,
respectively. We are given the objective to learn this task by using knowledge
from a source domain $(D_{s})$ and task $(T_{s})$. thus,transfer learning aims
to learn target task by a predictive function $f_{T}(.)$ by discovering and
then transferring knowledge from source domain, task, where $D_{s}\neq
D_{T}.$and $T_{s}\neq T_{t}$ and size of source domain is larger than target
domain $N_{s}\geqslant N_{t}$.

Definition 2.2:Let $(D_{s},D_{T},T_{s},T_{t},f_{T}(.))$ resemble transfer
learning. Then, it is considered deep transfer learning when $f_{T}(.)$
represents non-linear function with deep neural network.

Figure 5:  Typical steps involved in a GAN

Definition 2.3: In Adversarial domain adaptation,’ learning process involves
two-player game. Let $G_{d}$ be domain discriminator and first player. The
first player is responsible for separating source from the destination. Let
Let $G_{f}$ be feature extractor and second player. Second player always tries
to confuse domain discriminator in separating domain- invariant features. The
two players play adversarially: Feature extractor is optimized by maximizing
loss of domain discriminator. On the other hand, domain discriminator is
optimized by minimizing its loss.

The loss function is expressed as

$L(\theta_{f},\theta_{y},\theta_{d})=\frac{1}{n}\sum`_{{}^{\prime}x\epsilon
D}L_{y}(G_{y}(G_{f}(x_{i})),y_{i})-\frac{\lambda}{n_{s}+n_{t}}\sum`_{{}^{\prime}x\epsilon(D_{s}\cup
D_{t})}L_{y}(G_{y}(G_{f}(x_{i})),y_{i})$]

$\lambda,L_{y}L_{d},d_{i}$ be trade/off parameter, label classifier loss,
domain discrimination loss, domain label of input samples respectively.

A saddle point in terms of $\theta_{f},\theta_{y},\theta_{d}$ reached after
training is as follows

$(\theta_{f},\theta_{y},\theta_{d})=argmin_{\theta_{f},\theta_{{}^{\prime}y}}L(\theta_{f},\theta_{y},\theta_{d})$

$(\theta_{d})=argmax{}_{\theta_{f},\theta_{y}}L(\theta_{d})$

Figure 6: Typical steps in Deep transfer Learning

.

###### Lemma 1

Ethnic engagement classification is amenable to deep transfer learning.

Proof:Transfer learning is capable of making a synergy in between different
task/domains without the need of manual labelling.To reduce distribution
imbalance across domains, deep neural network uses its ability of learning
abstract representations, such that meaningful context underneath data is
discovered, which in turn help to transfer knowledge from source to
destination. Thus, ethnic engagement classification is amenable to deep
transfer learning problem.

###### Lemma 2

’ Ethnic engagement classification is amenable to adversarial DEEP CNN

Proof:Deep neural networks learns hierarchical and abstract feature
representation, with a view of merely reducing, rather than completely
deducing cross=domain imbalance. Unfortunately, there is risk behind the
assumption that source and target sharing the equal label space. In this
context, deep convolutional neural network learns such deep features by deep
abstraction layers in confusing discriminator on adversarial training process.
Thus, ethnographic engagement classification is amenable to deep CNN based
transfer learning scheme.

###### Lemma 3

Ethnic engagement classification is amenable to Unsupervised Transfer Learning
problem

Proof: Unsupervised transfer learning is characterized by the fact that target
is part of but different than source. However, it is assumed that no labeled
knowledge is present in either source or target.Let $D_{s},T_{s}$be source
domain and learning task . We also assume that $D_{t},T_{t}$be target domain
and learning task, respectively.Unsupervised learning intends to improve the
outcome of target predictive function $f(.)$ in $D_{T}$ by using the knowledge
acquired from $D_{S}$and $T_{S}$, where $T_{S}\neq T_{T}$

In a classroom assessment, for example, it is difficult for a teacher to
understand whether a specific mathematical problem (target domain) is solvable
by native or ethnic minority group . However, a teacher cross-checks the
solving abilities of separate groups of ethnic and native students in other
assessments from science courses (source domain). In this way, a teacher
understands whether ethnicity is the reason behind a student’s failure in math
problem solving. Thus, ethnic engagement classification is amenable to
unsupervised transfer learning problem.

##  III Proposed Deep Transfer Learning Scheme

In this section, we propose our Deep Neural Network based Adversarial Transfer
Learning scheme with theoretical justification. We begin by describing
different steps of proposed algorithm ETHNO-DAANN. Then, we justify its
adversarial training approaches.

###  III-A ETHNO-DAANN Algorithm

Data: Training set $D=\\{(I_{1},y_{1}),(I_{2},y_{2}),(I_{n},y_{n})\\}$ of
ethnographic information, where $y_{i}$ is a class label information of input
$I_{i}$

Result: vector $\hat{y}$ resembling predicted class

for  _$i=0;\ i <n;\ i=i+1$_ do

Group input samples according to class label ;

end for

Process data samples for balancing classes;

Do GAN training on balanced inter-class data;

for  _$i=0;\ i <n;\ i=i+1$_ do

Do translation using GAN ;

end for

Do merging on under-rated class and training data;

shuffle;

Do model training on balanced training data;

Evaluate model on test data to predict class;

Algorithm 1 ETHNO-DAANN

Lets describe the different steps of proposed ETHNO-DAANN algorithm (Algorithm
1). Let us consider we have input set having textual, visual and video
information regarding student engagement to digital platform.We first try to
group data by making classes of similar attributes in same umbrella. In this
process, we conduct necessary measurement such as re sampling for facilitating
the reduction across class samples.Next phase involves training of adversarial
model to learn imbalance across classes. Actually, this process involves
transformation in between those classes ,where adversarial model is capable of
learning features of under-rated classes from highly-represented classes. As
an outcome of the complete transformation, balanced classes are discovered
where we have original and generated data.Final step involves the
classification with Deep convolutional neural network. In this process,
training model is justified with test data for class prediction.

###### Theorem 4

ETHNO-DAANN classifies ethnicity information with adversarial training.

Proof: In ETHNO-DAANN, generator is created as a feature extraction tool for
targets. Learning goes ahead by feature extraction from data of two domains
and as such, discriminator is unable to figure out data source. With the
assistance of GAN, ETHNO-DAANN is capable of classification with adversarial
training.The loss function is represented by $l=l_{C}(D_{S}+y_{s})+\lambda
l_{D}(D_{s},D_{t})$\- where $l_{c}$ and $l_{d}$ denote net loss and domain
discriminator loss respectively.

###### Theorem 5

ETHNO-DAANN is a sub-class of of adversarial itransfer network. [

Proof:ETHNO-DAANN reproduces /produces features which distinguish features as
well, where features are generated from separate domains. The process ends up
with making discriminator as confused as possible.The loss function is written
as

$l_{d^{\prime}}=max[-\frac{1}{N}\sum_{I=1}^{N}L_{d}^{i}(W,b,u,z)-\frac{1}{n}\sum_{i=n+1}^{N}L_{d}^{i}(W,b,u,z)]$

where $(W,b)$is a matrix pair and $(u,z)$ is a vector-scalar pair.

###### Lemma 6

ETHNO-DAANN is a variant of domain separation network.

Proof: Let source and target domain be representatives of public and 1public
services. We assume that common features are represented by source ’domain,
while features which are independent across domains -are called private
services.-Then by considering the training loss, reconstruction loss,
similarity loss and difference loss across domains, the overall loss function
is projected.

The loss is written as

$l=l_{task}+\alpha l_{recon}+\beta l_{difference}+\gamma l_{smilarity}$

where $l_{task},\alpha l_{recon};\beta l_{difference},\gamma l_{smilarity}$ be
training loss, reconstruction loss, loss of difference between two sectors and
similarity loss of public sector within source and target domains.

##  IV Survey

In this section, we present the survey questions prepared toward formulating
some-form of hypotheses regarding student ethnographic information in
classroom environment. This is required for initially screening some ethnic
features, such as nationality, social-status, age of students, which can be
exploited in our deep neural network based classifier.

Question 1:  Have you found any difference, when you are teaching among ethnic
groups of homogeneous or mixed categories ?

Question 2:  Do you think you have to prepare your lessons especially
according to the requirement of ethnic group?

Question 3:  Do you see any difference when you teach foreign citizens or
minority or trial groups?

Question 4:  Have you prepared yourself differently for lessons for
foreigners, minorities or trial groups?

Question 5:  Do you think feedback or reactions of students of ethnic groups
differ according to their age?

Question 6: Have you seen the same type of study attention between female or
male students in minority groups?

Question 7: What are the key barriers (behavioral phenomena)you found most
common in ethnic group kid?

Question 8:  Do you feel that curriculum or resources developed/available for
ethnic groups is enough as teaching material?

Question 9:  Have you thought about using assistance from social scientists or
external references in mediating uncomfortable moments with ethnic grou’ps?

The survey is conducted to asses whether migrants are welcome to education
system.It also reflects whether teachers and students 0are able to adjust with
new classroom environment with ’different than parents’ children The questions
are asked to evaluate whether teachers come across any difficulties, other
than language while teaching ethnic group. Their reactions such incidence is
understood by some feedback, whether they would sit with different student
privately or leave it away. The survey conversations also help to understand
whether they take a problem child as with native kid, or they prefer to send
them to special clustered school. The questions would also help urban
policymakers to reassess the social- development policy applicable to
communities. Policymakers are generally found to arrange or organize special
foreign kid students in vision of welcoming diversified citizens.Steps are
taken by government in adjusting social status any deprived ethnic community.

##  V Conclusion

In this paper, we conduct research about ethnographic information for
motivating students, necessitated by post-communist education reform and the
fourth industrial revolution. As such, we develop an adversarial training
based deep transfer learning algorithm namely ETHNO-DAANN to infer ethnic
features inherent in classroom students. We also present survey questions
prepared to formulate hypotheses from teachers’ opinions when they interact
with students in problem-solving. Theoretical analysis is also presented on
how ethnicity classification is amenable to unsupervised, deep and transfer
learning problems and how ETHNO-DAANN solves it with adversarial, deep CNN
based approach.

References

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English as a Foreign Language Classroom.

2.Fónadová, L., Katrňák, T. and Simonová, N., 2019. The Czech Republic: From
ethnic discrimination to social inclusion in the educational system. In The
Palgrave handbook of race and ethnic inequalities in education (pp. 379-419).
Palgrave Macmillan, Cham.

3.Kohout-Diaz, M., 2018. School marketing and segmentation: A comparative
study in Bulgaria and the Czech Republic. International Journal of Educational
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4.Messing, V., 2017. Differentiation in the making: Consequences of school
segregation of Roma in the Czech Republic, Hungary, and Slovakia. European
Education, 49(1), pp.89-103.

5.Obrovská, J., 2018. The Future of Roma Education in the Czech Republic: From
Homogenization to Diversification?. In Roma Identity and Ritual in the
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6.Neumann, E., 2022. Problem students, problem classes: Polarization,
differentiation and language about disruptive student behaviour in Hungarian
primary schools. Intersections. East European Journal of Society and Politics,
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A survey on deep transfer learning. In International conference on artificial
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# Spatial scales of COVID-19 transmission in Mexico

Brennan Klein<EMAIL_ADDRESS>Network Science Institute, Northeastern
University, Boston, Massachusetts, USA  Laboratory for the Modeling of
Biological & Socio-technical Systems,
Northeastern University, Boston, Massachusetts, USA  Harrison Hartle  Network
Science Institute, Northeastern University, Boston, Massachusetts, USA  Munik
Shrestha  Network Science Institute, Northeastern University, Boston,
Massachusetts, USA  Ana Cecilia Zenteno  Massachusetts General Hospital,
Boston, Massachusetts, USA  David Barros Sierra Cordera  Instituto Mexicano
del Seguro Social, Ciudad de México, México  José R. Nicolas-Carlock
Instituto de Investigaciones Jurídicas, Universidad Nacional Autónoma de
México,
Ciudad de México, México  Ana I. Bento  Department of Epidemiology and
Biostatistics, School of Public Health,
Indiana University, Bloomington, Indiana, USA  Benjamin M. Althouse
Information School, University of Washington, Seattle, Washington, USA
Department of Biology, New Mexico State University, Las Cruces, New Mexico,
USA  Bernardo Gutierrez  Department of Biology, University of Oxford, Oxford,
UK  School of Biological & Environmental Sciences,
Universidad San Francisco de Quito, Quito, Ecuador  Consorcio Mexicano de
Vigilancia Genómica  Marina Escalera-Zamudio  Department of Biology,
University of Oxford, Oxford, UK  Consorcio Mexicano de Vigilancia Genómica
Arturo Reyes-Sandoval  The Jenner Institute, University of Oxford, Oxford, UK
Instituto Politécnico Nacional, IPN, Ciudad de México, México  Oliver G. Pybus
Department of Biology, University of Oxford, Oxford, UK  Department of
Pathobiology and Population Science, Royal Veterinary College, London, UK
Pandemic Sciences Institute, University of Oxford, UK  Alessandro Vespignani
Network Science Institute, Northeastern University, Boston, Massachusetts, USA
Laboratory for the Modeling of Biological & Socio-technical Systems,
Northeastern University, Boston, Massachusetts, USA  Jose Alberto Diaz-
Quiñonez<EMAIL_ADDRESS>Instituto de Ciencias de la Salud,
Universidad Autónoma del Estado de Hidalgo,
Pachuca, Hidalgo, México  Samuel V. Scarpino<EMAIL_ADDRESS>Network Science Institute, Northeastern University, Boston, Massachusetts, USA
Institute for Experiential AI, Northeastern University, Boston, Massachusetts,
USA  Santa Fe Institute, Santa Fe, New Mexico, USA  Moritz U.G. Kraemer
<EMAIL_ADDRESS>Department of Biology, University of Oxford,
Oxford, UK  Pandemic Sciences Institute, University of Oxford, UK

###### Abstract

During outbreaks of emerging infectious diseases, internationally connected
cities often experience large and early outbreaks, while rural regions follow
after some delay [1, 2, 3, 4, 5, 6]. This hierarchical structure of disease
spread is influenced primarily by the multiscale structure of human mobility
[7, 8, 9]. However, during the COVID-19 epidemic, public health responses
typically did not take into consideration the explicit spatial structure of
human mobility when designing non-pharmaceutical interventions (NPIs). NPIs
were applied primarily at national or regional scales [10]. Here we use weekly
anonymized and aggregated human mobility data and spatially highly resolved
data on COVID-19 cases, deaths and hospitalizations at the municipality level
in Mexico to investigate how behavioural changes in response to the pandemic
have altered the spatial scales of transmission and interventions during its
first wave (March - June 2020). We find that the epidemic dynamics in Mexico
were initially driven by SARS-CoV-2 exports from Mexico State and Mexico City,
where early outbreaks occurred. The mobility network shifted after the
implementation of interventions in late March 2020, and the mobility network
communities became more disjointed while epidemics in these communities became
increasingly synchronised. Our results provide actionable and dynamic insights
into how to use network science and epidemiological modelling to inform the
spatial scale at which interventions are most impactful in mitigating the
spread of COVID-19 and infectious diseases in general.

Table 1: Policy summary Background |  The establishment, persistence and growth rates of COVID-19 mainly depend on human mobility and mixing. However, current approaches attempting to limit transmission have been primarily based on administrative boundaries instead of the natural scales of human mobility.
---|---
Main findings
& limitations |  Using aggregated and anonymized human mobility and detailed COVID-19 case data, we find that the scales of human mixing shift during the pandemic and that transmission is highly clustered amongst mobility communities.
Policy
implications |  Structuring interventions based on spatial mobility may be more effective compared to interventions based on administrative boundaries. Future pandemic control interventions should consider empirical human mobility networks when designing interventions.

##  1 Introduction

The transmission of infectious diseases is highly heterogeneous. Differences
in population structure, the landscape of prior immunity, and environmental
factors, result in differences in the timing of outbreaks, their magnitude,
and duration [2, 11, 3, 9, 12, 13, 14, 15, 16, 17, 18, 19, 20]. For infectious
diseases, one principal component determining the spatial structure of
outbreaks is the frequency of interactions between susceptible and infectious
individuals within and between regions. In most geographies, public health
decision-making authority follows political boundaries. However, from an
epidemiological perspective, the relevant spatial units may not strictly
follow political boundaries but rather human mixing [8, 14, 21]. Evaluating
the spatial structure of COVID-19 transmission remains important in
determining optimal interventions (non-pharmaceutical and/or vaccination) to
reduce transmission and limit the risk of resurgence of cases [22, 23, 24,
25].

During the first half of 2020, Mexico experienced one of the largest SARS-
CoV-2 epidemics worldwide, with more than 600,000 cases and 65,000 confirmed
deaths reported between February and September 2020 [26] (Fig. 1a). The
epidemic wave peaked in May in the largest metropolitan areas of Mexico City
and the State of Mexico and later ignited epidemics in all other states [27],
peaking between June and July 2020 (Fig. 1b). Here we combine municipality
level epidemiological data with weekly anonymized aggregated human mobility
data at the same scale, to characterise the spatial scales of the Mexican
COVID-19 pandemic and their implications for the implementation of spatially
targeted interventions.

##  2 Results

###  2.1 Spatial expansion of COVID-19 in Mexico

In Mexico, the spatial range of transmission expanded rapidly after reports of
the earliest cases in March 2020, with over 700 municipalities reporting
transmission by July 2020 (out of 2,448, Fig. 1c). During April and May the
risk of positive RTq-PCR confirmed cases amongst men aged 30-69 was 1.4 times
higher than between July 1 and September 1 (Fig. 1d,e), indicating that the
epidemic spread initially within and through these age groups (Extended Data
Figure A.1). This dynamic trend in the demographics of cases is similar to
that observed in other countries during the early stages of the pandemic [28,
29].

Figure 1: Epidemiological situation of COVID-19 in Mexico. (a) Map of
cumulative cases per 100,000 people, as of September 1, 2020. (b) Timeline of
new cases per 100,000 population at the state level (7-day rolling average),
highlighting the 15 states with the most severe cumulative outbreaks. (c)
Number of municipalities that reported confirmed cases of COVID-19 through
time. (d) Age and sex distributions of confirmed COVID-19 cases across Mexico,
highlighting “early” and “late” periods during which the relative risk of
infections were calculated. (e) Age and sex relative risk ratios of infection,
comparing the early vs. late periods from panel (d).

States that experienced early transmission were the state of Mexico and Mexico
City (Fig. 1b) [27]. Due to the centrality of Mexico City connecting people
from abroad (international arrivals) and within Mexico we hypothesise that
human mobility from these states was a key driver of the spread of COVID-19 in
Mexico. Using anonymized, opt-in and aggregated human movement data from
mobile phones (Materials and Methods) we find that case growth rates across
Mexican states were well predicted by a lagged model of human movements from
the State of Mexico and Mexico City between March and May 2020 (Fig. 2c,
conditional $R^{2}=0.62$; see Materials & Methods). Further, we observe that
the share of overall relative human mobility to and from Mexico and Mexico
City increased markedly during that period (Fig. 2b) when overall human
mobility between states declined (Fig. 2b, Extended Data Figure A.2 showing
state level data on change in human mobility). This points towards a change in
the network structure of human mobility in Mexico, as documented in some other
countries [30, 31]. Overall transmission, and the importance of Mexico City
driving the epidemic, declined after the implementation of NPIs through May
2020. However, after the lifting of physical distancing measures on June 1st
(see table of documented changes in NPIs, Table LABEL:tab:s1), case growth
rates in the country increased again as a function of mobility from Mexico
City, in line with models predicting that lifting lockdowns can lead to
reseeding of transmission chains from larger to smaller cities where epidemics
were successfully controlled (Fig. 2b, Table LABEL:tab:s1, [7]).

Figure 2: Human mobility and transmission of COVID-19 in Mexico. (a) Pre-
pandemic average of the inter-municipality mobility network, coloured by
network community (detected using the Infomap algorithm). Mobility flow data
is based on the aggregated Google Mobility Research dataset (see Materials &
Methods). (b) Percent of typical weekly mobility nationwide (typical refers to
mobility between January 12 and February 29, 2020). (c) Evolution of the
coefficients of mobility flow from Mexico City in (lagged) correlations with
state-level case rates across the country, highlighting the key role that
mobility from Mexico City played in the early stage of the epidemic. (d)
Average fraction of total outgoing mobility from each state that is to Mexico
City (black) and the median entropy of states’ distributions of outgoing
mobility. Error bands correspond to 95% confidence intervals.

Variation in weekly new cases within each state in Mexico are generally well
predicted by cases in Mexico City weighted by human mobility except for Baja
California, Morelos, Chihuahua, Oaxaca, and Chiapas (Extended Data Figure
A.3). We hypothesise that epidemics there were possibly seeded from other
countries (USA and Guatemala); further SARS-CoV-2 genomic analyses of unbiased
collections of samples will be needed to confirm the SARS-CoV-2 lineage
dynamics in these states [27, 32, 33, 34, 35, 36]. Human mobility data showing
cross border (US to Mexico) movements indicate higher overall mobility to
bordering states in Mexico and growth rates in US-Mexico border states appear
higher in the period between 24 May - 28 June 2020 (Extended Data Figures A.4,
A.5, A.6). The high mobility during that phase resulted in larger case numbers
in states bordering the US when compared to other states in Mexico (Extended
Data Figure A.5).

Figure 3: Network structure determines the synchrony of epidemics. (a)
Grouping of municipalities based on the state administrative boundaries.
Shaded municipalities are removed from downstream analyses as they could not
be assigned a movement community (see Materials & Methods). (b) Example
grouping of municipalities based on human movement data and a community
detection algorithm [37] (Materials and Methods). Colours indicate movement
communities. Grey municipalities have limited recorded movements and could not
be assigned to a community and were consequently excluded from analysis. (c)
Synchrony of weekly growth rates of epidemics across municipalities as
measured by the pairwise standard error between growth rates. The lower the
error, the more synchronised epidemics are. Blue line shows grouping by
network communities, and orange shows groupings by state administrative
boundaries. The green dashed line shows the nationwide trend in reported cases
during this period. For a visual intuition of the differences in within-
community standard deviations of growth rates, see Extended Data Figure A.9.

###  2.2 The scales of COVID-19 transmission

It is well known that reductions in mobility (a proxy for reductions in
population mixing) have reduced the transmission of COVID-19 within a location
[38]. However, it remains unclear how structural changes to the mobility
network (shifts in the frequency and intensity of mobility within and among
regions) have impacted COVID-19 dynamics empirically [30, 39, 40, 41, 31]. Our
underlying hypothesis is that more tightly connected communities exhibit more
synchronised epidemic dynamics and, conversely, that more disjointed
individual communities have less synchronised epidemics and their epidemics
are more likely to fade out [4, 5, 6] (here, communities are equivalent to
municipalities and synchrony is defined as the similarity among communities in
weekly case growth rates [42]). Both processes have critical implications for
disease mitigation and eliminations locally, and at a country level [7, 43,
44, 45, 46, 47]. The Mexican government announced stringent physical
distancing policies on March 30th, 2020 which resulted in marked changes in
the mobility network (Fig. 2a, Table LABEL:tab:s1).

To quantify the degree to which mobility patterns are structured by
geopolitical boundaries, we use a community detection algorithm that groups
municipalities based on their movement patterns [48]. Specifically, we aim to
identify groups of municipalities such that movements between municipalities
within the same group, i.e., community, are more frequent than movements to
other municipalities in other communities. Community detection is often
accomplished via modularity maximization [49]; however, these approaches
neglect information about the flow of mobility through the network. Instead,
we leverage the map equation via an algorithm called InfoMap [48]. The InfoMap
algorithm utilises an information theoretic approach to derive expected
connectivity patterns if the observed flows were entirely determined by a
random walk process. For this study, InfoMap is ideal because it is
conceptually related to infectious disease transmission models, which often
also utilise stochastic processes [50].

The aim is to identify municipalities where frequent interactions between
individuals occur, such that the detected communities approximate the spatial
scales of disease transmission (i.e., communities in which it is assumed that
infection spreads via contacts within a relatively homogeneously mixing
population [51]). Accounting for spatial heterogeneity is known to be
important for assessing strategies for interventions [2], especially in areas
that have marked differences in urban and rural areas [52]. Using this
algorithm, we identify 16 communities before the first cases of COVID-19 were
detected in Mexico (Fig. 3b). Community size and organisation changed
following the announcement of the lockdown (March 23 and 30, 2020) in Mexico
and communities generally became smaller (fewer municipalities within each
community (Extended Data Figures A.7 and A.8 show the communities for each
week during the study period). At the peak of the lockdown, we identified
approximately 60 movement communities (a 4-fold increase from the baseline
period).

More specifically, there are two notable shifts in the network following the
introduction of NPIs. First, more communities are identified but importantly
the size of these communities shrinks disproportionately so that one community
expands (Mexico City) and many very small ones emerge (Fig. 2d). Further, as a
result of the lockdown human movements across municipalities decline more
rapidly than movements within a community with one important exception: Mexico
City. There we observe that the ratio of within municipality movements
declines at a similar rate than movements across municipalities (Extended Data
Figure A.2) further proving its central importance in the mobility network in
Mexico.

We then compared the weekly infection incidence growth rates within each
community and contrasted them to growth rates under a scenario in which
municipalities are grouped based on state boundaries (black lines, Fig. 3a,b).
As expected, we find that epidemics in municipalities that are grouped by
human mobility were more synchronised compared to those grouped by state (Fig.
3c; see Extended Data Figure A.9 for an illustration of the variance in
municipality epidemic growth rates for several example groups of
municipalities defined by administrative or network boundaries). The synchrony
among municipalities within each community were maximised in April and May
2020, a period when cases were rapidly rising across the country. After June,
epidemics that are grouped by movement are still more synchronised, but the
differences with groupings by state appear to be smaller (Fig. 3c). This later
period (June to October 2020) is a time when Mexico City appears to also lose
importance in seeding the epidemic across the country, and local factors
(e.g., population size) become more important in determining the epidemic
trajectory [53]. These results are expected as local factors become more
influential in determining disease dynamics (population size, local mixing)
and that the importance of continued virus re-importations wanes through time
[33].

##  3 Discussion & Limitations

We present a generalisable approach for understanding the spatial structure of
transmission of COVID-19 and other emerging infectious diseases by accounting
for the variations of the human mobility network. We aimed to differentiate
the transmission dynamics at a level defined by administrative boundaries from
that defined by simple community detection algorithms that are applied to
aggregated anonymized weekly human mobility data. We find that as human
mobility network structures change, so does to spatial transmission.
Incorporating these findings into real-world public health decision-making may
result in more effective strategies to control an epidemic [54, 55, 56, 57].
The European Commission for example published a report on Mobility Functional
Areas (MFAs) which were informed by mobile phone data but the adoption of
these recommendations remained sparse [55].

Our model and results are only as accurate as the data that go into them. The
Mexican COVID-19 database may suffer from underreporting due to testing
shortages, changing case definitions and spatial heterogeneity in reporting
[58, 59]. For example, relatively few cases were reported from Oaxaca (Fig.
1a) which may be due to barriers to access to testing [60]. Future extensions
of the model and as the pandemic continues will need to take into account
high-resolution SARS-CoV-2 cross-immunity. Further, our model is based on
higher level descriptions of the population (raw case data and population
level human movement data) and these do not capture the high contact
heterogeneity within each municipality (e.g., demographic heterogeneity and
assortative mixing) shown to be important in the transmission of COVID-19
[61]. Contact patterns may differ significantly by age group, employment
status and other factors not accounted for in this work. We did however
observe heterogeneity in the demographic makeup of cases during the earlier
phases of the Mexican COVID-19 pandemic.

Further, results should be interpreted in light of important limitations
related to the human mobility data. First, the Google mobility data is limited
to smartphone users who have opted into Google’s Location History feature,
which is off by default. These data may not be representative of the
population as whole, and furthermore their representativeness may vary by
municipality. Importantly, these limited data are only viewed through the lens
of differential privacy algorithms, specifically designed to protect user
anonymity and obscure fine detail.

Mexico is composed of 31 free and sovereign states and Mexico City, united
under a federation. This means that each administrative region or state is
governed by its own constitution, although they are not completely independent
of the federal jurisdiction. Furthermore, each state is divided into
municipalities, the nation’s basic administrative unit, which possesses
limited autonomy (discretionary power on how best to respond to, or apply a
public policy). Under a serious nationwide health threat or emergency, such as
a pandemic, the federal Ministry of Health (MoH) acquires full authority over
the health policies to be implemented nationwide. Nevertheless, Mexican law
establishes that the General Health Council (GHC), a collegial body that
reports to the president of the republic has the character of health
authority, and can emit obligatory norms to be abided by the MoH. The GHC is
presided by the Minister of Health, and is conformed by federal institutions
(e.g.h, Economy, Communication & Transport) as well as academic institutions,
representatives from pharmaceutical industry, and other health system actors
[62]. Given its mandate and position in the Mexican health system, the GHC
constitutes a promising agent to drive public policy outside of the margins or
across geo-administrative units. Furthermore, there are examples of inter-
state and inter-municipality coordination to resolve problems that extend
beyond their borders such as waste management, tax, policing, and perhaps most
relevant, health provision. It is in these contexts where evidence-based
interventions on innovative approaches, such as the ones presented here become
not only an option but a possibility, with greater impact in reducing
transmission as compared to approaches where interventions are based on
administrative boundaries. However, theory often differs from practice and
reality brings along additional and expected factors into play (e.g., economic
[63] and political interests) many of which are not accounted for in this
work. Some state governors for example refused to comply with federal health
policies in the early relaxation phase in May 2020 [64].

Mexico has suffered a large and devastating epidemic, and we hope that our
findings contribute to a more rational implementation of interventions in the
future that can account for the substantial and changing spatial heterogeneity
in transmission. Such analyses can be updated and translated to any other
country in the world for which aggregated human mobility data is available.
Future work should also focus on validating the inferred spatial scales with
genomic data [32, 33, 65] or other coarse-graining techniques [66, 67].
Developing interventions using patterns observed in empirical mobility
networks must be added to the list of priorities for pandemic response and
preparedness in the 21st century.

##  4 Materials & Methods

#### Epidemiological data:

Epidemiological data include individual level information on patients with
confirmed RTq-PCR COVID-19 infection between March - September 30th, 2020.
Data were downloaded from
http://datosabiertos.salud.gob.mx/gobmx/salud/datos_abiertos/datos_abiertos_covid19.zip
(last accessed October 24, 2020). Data include information about patients
demographics (age and sex) and municipality of residence. In all analyses we
used the date of onset of symptoms.

#### Population and travel data:

Human mobility and population data were extracted at the municipality level
based on the 2016 boundaries (INEGI 2016:
https://www.inegi.org.mx/app/mapa/espacioydatos/default.aspx). Population data
were downloaded from the COVID-19 indicator dataset, which was provided by
INEGI (https://www.inegi.org.mx/investigacion/covid/).

#### Aggregated and anonymised human mobility data:

We used the Google COVID-19 Aggregated Mobility Research Dataset described in
detail in [68, 69], which contains anonymized relative mobility flows
aggregated over users who have turned on the Location History setting, which
is turned off by default. This is similar to the data used to show how busy
certain types of places are in Google Maps—helping identify when a local
business tends to be the most crowded. The mobility flux is aggregated per
week, between pairs of approximately 5km2 cells worldwide, and for the purpose
of this study further aggregated for municipalities in Mexico.

To produce this dataset, machine learning is applied to log data to
automatically segment it into semantic trips. To provide strong privacy
guarantees [70], all trips were anonymized and aggregated using a
differentially private mechanism to aggregate flows over time (see
https://policies.google.com/technologies/anonymization). This research is done
on the resulting heavily aggregated and differentially private data. No
individual user data was ever manually inspected, only heavily aggregated
flows of large populations were handled. All anonymized trips are processed in
aggregate to extract their origin and destination location and time. For
example, if $n$ users travelled from location $a$ to location $b$ within time
interval $t$, the corresponding cell $(a,b,t)$ in the tensor would be $n\pm
err$, where $err$ is Laplacian noise. The automated Laplace mechanism adds
random noise drawn from a zero mean Laplacian distribution and yields
$(\epsilon,\delta)$-differential privacy guarantee of $\epsilon=0.66$ and
$\delta=2.1\times 10^{29}$ per metric. Specifically, for each week $W$ and
each location pair $(A,B)$, we compute the number of unique users who took a
trip from location $A$ to location $B$ during week $W$. To each of these
metrics, we add Laplace noise from a zero-mean distribution of scale $1/0.66$.
We then remove all metrics for which the noisy number of users is lower than
100, following the process described in [70], and publish the rest. This
yields that each metric we publish satisfies $(\epsilon,\delta)$-differential
privacy with values defined above. The parameter $\epsilon$ controls the noise
intensity in terms of its variance, while $\delta$ represents the deviation
from pure $\epsilon$-privacy. The closer they are to zero, the stronger the
privacy guarantees.

These results should be interpreted in light of several important limitations.
First, the Google mobility data is limited to smartphone users who have opted
into Google’s Location History feature, which is off by default. These data
may not be representative of the population as whole, and furthermore their
representativeness may vary by location. Importantly, these limited data are
only viewed through the lens of differential privacy algorithms, specifically
designed to protect user anonymity and obscure fine detail. Moreover,
comparisons across rather than within locations are only descriptive since
these regions can differ in substantial ways.

#### Timeline of interventions:

The Mexican government has outlined four principle objectives for the control
of COVID-19: a) Reduce risk of acquiring infection, b) Reduce risk of severe
morbidity and mortality, c) Reduce risk and impact on society and d) Reduce
risk of transmission between infectious and susceptible individuals. We
collated a full list of interventions between February and September 2020 and
details are provided in Table LABEL:tab:s1, including references.

#### Relative risk model:

Following Goldstein and Lipsitch [71] we used age stratified epidemiological
data to assess the temporal shifts in the share of a given age group among all
cases of infection. To do so we use the relative risk (RR) [72, 73] statistic
that estimates the ratio of the proportion of a given age group among all
detected cases of COVID-19 for a later time period vs. an early time period.
We selected the early time period to be the month of April (the period right
after the implementation of the lockdown) and the late period to be June to
September. We adopted the code and model from Goldstein and Lipsitch described
in detail [71].

#### Community detection algorithm:

Human mobility networks, based on data from mobile devices, can be used to
capture important population-level trends. Microscopic descriptions often
remain too complex to extract meaningful information to describe the
transmission process accurately [61]. We here use a community detection
algorithm following [48] to identify human movement communities (basins) where
within-community mobility among municipalities is higher than across-community
mobility. We chose this community detection algorithm as it is conceptually
related to infectious disease transmission models—both utilising random walks.

#### Municipality level case growth rates:

To estimate the daily epidemic growth rates in each municipality, we fit a
mixed effects GLM of log new daily case counts in sliding 7-day windows (fixed
effect; approximately the generation time of COVID-19 in the earliest wave)
and a random effect for each municipality on the slope and intercept, using
the R package lme4 v.1.1-21 [74]. Daily case counts were determined using the
date of symptom onset.

#### Relationship between case growth rates and mobility:

To test for an effect of mobility from Mexico City on municipality growth
rates, we fit a mixed effect GLM with log mobility as a fixed effect, a random
effect on the intercept for each municipality and a random effect on the slope
and intercept for log mobility each week. The conditional and marginal
coefficient of determination, i.e., $R^{2}$, were calculated using the R
package MuMIn v1.471. [75] which implements the method developed by Nakagawa
et al. 2017 [76]. Model selection was performed using analysis of variance for
mixed effects models as implemented in the R package lmerTest v.3.1-3 [77].

## Additional information

#### Acknowledgments:

We thank all health care workers and those involved in the collection,
processing and publishing COVID-19 epidemiological data from Mexico.

#### Funding:

M.U.G.K., O.G.P., B.G. acknowledge funding from the Oxford Martin School
Pandemic Genomics programme. M.U.G.K. acknowledges funding from the European
Horizon 2020 programme MOOD (grant no. #874850), the Wellcome Trust, a Branco
Weiss Fellowship, The Rockefeller Foundation and Google.org. The contents of
this publication are the sole responsibility of the authors and do not
necessarily reflect the views of the European Commission or the other funders.
B.K., H.H., S.V.S., & A.V. acknowledge the support of a grant from the John
Templeton Foundation (61780). The opinions expressed in this publication are
those of the author(s) and do not necessarily reflect the views of the John
Templeton Foundation.

#### Author contributions:

S.V.S., M.U.G.K. and B.K. developed the idea, planned the research and
conducted analyses. A.C.Z. and D.B.S.C. collected government intervention
data. S.V.S., M.U.G.K. and B.K. wrote the first draft of the manuscript. All
authors interpreted the data, contributed to writing and approved the
manuscript.

#### Competing interests:

We declare no conflicts of interest.

#### Data and materials availability:

Code, spatial, and epidemiological data are available upon publication. The
Google COVID-19 Aggregated Mobility Research Dataset used for this study is
available with permission from Google LLC. Correspondence and requests for
materials should be addressed to B.K., J.A.D-Q., S.V.S., or M.U.G.K.

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##  Appendix A Extended Data Figures

Figure A.1: Number of new cases per state and sex (7-day average).

Figure A.2: Weekly relative change in human mobility within each state and
between states (incoming and outgoing) as compared to baseline.

Figure A.3: State-specific correlations of new reported cases (weekly) vs.
mobility from Mexico City times new reported cases in Mexico City (weekly).
States with low mobility and case count data coverage are included but not
plotted in this figure.

Figure A.4: Weekly relative human mobility where the origin is the USA and the
destination are states in Mexico divided into states that share a land border,
Mexico and Mexico City and all other states.

Figure A.5: Weekly new cases per 100,000 divided into cases in Mexico City and
the state of Mexico, states that share a land border with the USA, and all
other states.

Figure A.6: Weekly number of cases among municipalities in Mexico coloured by
their geographic position to the USA (bordering vs. not bordering) and the sum
of in-municipality mobility $\times$ weekly new cases among origin nodes (both
on the log scale).

Figure A.7: Four-week snapshots of mobility in Mexico. Weekly human mobility
in Mexico at the municipality level. Thickness of lines represents intensity
of relative mobility flow. Colours represent the membership to movement
communities as estimated using the map equation (Materials & Methods).

Figure A.8: Number of communities detected each week during the first wave of
the COVID-19 epidemic in Mexico.

Figure A.9: For one example week (April 19, 2020), comparison of the mean
standard deviations in municipality growth rates within either (left)
administrative (states) boundaries or (right) network communities. In each
panel, the standard deviations of municipalities’ infection growth rates
within each grouping (state vs. network community) is shown on the right.
Figure 3c shows the average of these values over time.

Table A.1: Timeline of government interventions in Mexico. gray!70 Date |  Intervention
---|---
March 16, 2020 |  Mexican Secretariat of Public Education (SEP) suspend classes in schools of preschool, primary, secondary education, as well as those of the upper middle and higher types dependent on the SEP [78].
March 17, 2020 |  Universities begin to suspend classes and social events [79].
March 20, 2020 |  Mexican Secretariat of Public Education (SEP) cancels all civic and sports events [80].
March 21, 2020 |  United States - Mexico border was closed to non-essential travel but remained open for commerce. Closure extended until November 21, 2020 [81].
March 23/24, 2020 |  National period of social distancing begins. Schools closed and all non-essential operations were closed including gatherings of 100+ people [82].
March 30, 2020 |  National health emergency declared. Policies included: (1.) Non-essential services suspended. (2.) Private sector is asked to require employees to work from home. (3.) Sectors that kept operating normally: government, health (public and private), public safety, social programs, critical infrastructure, and essential services. A full list of essential services can be viewed here: https://www.dof.gob.mx/nota_detalle.php?codigo=5590914&fecha=31/03/2020. (4.) People over 60 years old are urged to stay home. (5.) Public gatherings of over 50 people are banned. (6.) There was no enforced curfew. Expiration date: April 30, 2020.
April 5, 2020 |  Hospital reconversion strategy guidelines published in order to contain nosocomial transmission [83]
April 16, 2020 |  The Federal Government announces the extension of the health emergency and emphasizes the need to restrict movement to and from areas of high transmissibility until May 30th [84].
May 14, 2020 |  Ministry of Health announces epidemiologic color-coded system to re-open social, educational, economic activities at state level [85].
May 18, 2020 |  First phase of the “new normality” 324 municipalities with no recorded COVID-19 cases are given green light to reopen businesses and schools [86]. Car factories were meant to reopen on June 1st, but began reopening on May 18th under US pressure (Factories remained closed from March 23rd, to May 18th).
June 1, 2020 |  Mexico’s national period of social distancing concludes. A new color-coded system was enacted across the country to assess how quickly states can reopen their economies and schools: red, orange, yellow, and green [87].
July 20, 2020 |  Daycare centers run by the country’s social security system reopened in coordination with local authority and based on color-coded indicators. Currently, all but 4 states have daycare centers open. [88]
October 18, 2020 |  The Health Ministry announced that 17 states—Mexico City included—were at alert level orange and 14 were at yellow. Only one state, Campeche, was at green [89]. In states at the orange level, businesses such as hotels and restaurants can reopen while following health protocols such as enforcing limited capacity. Yellow allows for most economic activities to return to normal with some occupancy limits.
October 22, 2020 |  Some local governments have chosen to enact more stringent restrictions than the federal government guidelines, e.g., Jalisco and Chihuahua [90].

###  A.1 Citation diversity statement

Recent work has quantified bias in citation practices across various
scientific fields; namely, women and other minority scientists are often cited
at a rate that is not proportional to their contributions to the field [91,
92, 93, 94, 95, 96, 97, 98]. In this work, we aim to be proactive about the
research we reference in a way that corresponds to the diversity of
scholarship in this field. To evaluate gender bias in the references used
here, we obtained the gender of the first/last authors of the papers cited
here through either 1) the gender pronouns used to refer to them in articles
or biographies or 2) if none were available, we used a database of common
name-gender combinations across a variety of languages and ethnicities. By
this measure (excluding citations to datasets/organizations, citations
included in this section, and self-citations to the first/last authors of this
manuscript), our references contain 3% woman(first)-woman(last), 22% woman-
man, 20% man-woman, 47% man-man, 0% nonbinary, 8% man solo-author, and 0%
woman solo-author. This method is limited in that an author’s pronouns may not
be consistent across time or environment, and no database of common name-
gender pairings is complete or fully accurate.